THE USE OF A FORMANT DIAGRAM IN AUDIOVISUAL SPEECH ACTIVITY DETECTION

Size: px
Start display at page:

Download "THE USE OF A FORMANT DIAGRAM IN AUDIOVISUAL SPEECH ACTIVITY DETECTION"

Transcription

1 THE USE OF A FORMANT DIAGRAM IN AUDIOVISUAL SPEECH ACTIVITY DETECTION K.C. van Bree, H.J.W. Belt Video Processing Systems Group, Philips Research, Eindhoven, Netherlands Karl.van.Bree@philips.com, Harm.Belt@philips.com ABSTRACT We present an audiovisual approach to the problem of voice activity detection for systems with a single microphone and a single camera with multiple people in the camera s field of view. We aim to have a speech activity detection result per person. The approach utilizes a face tracking and lip contour tracking algorithm for the video analysis, and pitch presence detection and formant frequency tracking algorithms for the audio analysis. When from the audio we detect speech activity and from the video we find lip activity for more than a single person, we check for each person whether the vowels correspond with the video mouth parameters to find out if this person speaks. To this end we make use of the F 1 - F 2 speech formant diagram in which we propose three vowel groups that are distinctive both from audio and video data. 1. INTRODUCTION For many speech signal processing applications such as speech telecommunications and speech recognition systems it is relevant to be able to detect speech activity. Speech activity detection algorithms like in [1] work well under good acoustic conditions but suffer from false detections when ambient noises are speech-like. Activity detection techniques purely on video lip motion like in [2, 3] aim to be independent of these noises, but suffer from false detections when people move their lips in facial expressions without talking. In this paper we adopt an audiovisual approach to the task of speech activity detection. We consider the case of multiple persons in a camera s field of view with only one of them talking at a time, while others could be moving their lips without talking. We aim to have a speech activity detection result per person. As summarized in Fig. 1, we propose to correlate speech features with mouth features to find proof of which person utters a detected vowel. In Section 2 we focus on the audio modality. An audio speech activity detector is given, and a 2-dimensional formant diagram is introduced in which we propose to distinguish three well-separated vowel groups. Section 3 deals The authors thank the reviewers A.C. den Brinker and R. Jasinschi from Philips Research Laboratories in Eindhoven, and R. Sluijter from the Eindhoven University of Technology for their useful comments. Figure 1. Flow chart of the audiovisual speech activity detector with the video modality. We present a lip detection and tracking algorithm. We introduce in the audio formant diagram distinguishable mouth shapes with the three vowel groups that we selected and use this in a lip detection algorithm. The main contribution of this paper is the specific choice of this diagram, and its application for the improved audiovisual activity detector presented in Section 4. Finally, in Section 5 we give our conclusions. 2. AUDIO VOICE SIGNALS AND DETECTION 2.1. Audio Speech Activity Detection We next describe the applied audio-only speech activity detector. In the first step we divide the signal into frames by windowing. Next, for each frame we investigate whether there is non-stationary signal activity. If so, the final step is to verify the presence of pitch. As such we will get one detection per audio frame. Let s[n] denote the sampled audio signal and B the audio frame size. We take B = 128 at F s = 8 khz. Let S w [k] be the M-points discrete Fourier transform (DFT) result of the Hanning windowed 2B last audio samples. We take M = 2B. P s [k] is the power spectrum: P s [k] = S w [k] 2. Note that, due to the symmetry in the frequency domain, only the first M/2 + 1 points of P s [k] are relevant. We estimate 2007 EURASIP 2390

2 from P s [k] the stationary background noise part P n [k] with a minimum statistics method [4]. We detect non-stationary signal activity when the SNR exceeds θ (we take θ = 8): M/2 k=0 P s[k] P n [k] M/2 k=0 P n[k] > θ. (1) The auto-correlation ρ[l] is calculated by the inverse DFT of P s [k] P n [k]. L = {l min,, l max } is the lag range corresponding to the frequency range of human pitch (between 80 and 500 Hz). Like in [1] we assume presence of pitch when ρ[l]/ρ[0] > θ ρ for any l L. (2) A good value for θ ρ is We detect speech when signal activity is detected according to Eq. 1 and when pitch presence is detected according to Eq. 2. To deal with consonants we keep the detection result positive for a small extended time period when pitch is no longer present and Eq. 1 is still satisfied. The audio-only speech detector works well for one person but it cannot discriminate between different people Voice Signals and Vowel Groups Next we link speech formant frequencies to vowels. The formant frequencies are denoted by F 1, F 2,.... In [5] (Figure 9), Peterson and Barney plot for ten vowels uttered by 76 speakers the location in an F 1 -F 2 diagram, and they then distinguish ten smoothly-shaped regions for each vowel. The figure demonstrates that already with the first two formant frequencies one can reasonably well predict the uttered vowel. In our further discussion we therefore restrict ourselves to F 1 and F 2. To estimate F 1 and F 2, we first perform DC-removal and pre-emphasis filtering. The signal is then Hanning-windowed. For each windowed audio frame a 10-th order auto-regressive (AR) model is calculated [6]. To find F 1 we search for the first (lowest) frequency in the range of 200 to 830 Hz at which the AR spectrum peaks with a sufficiently high Q- factor. We do the same for F 2 in the range of 500 to 2650 Hz. Compared to Peterson and Barney, we confine ourselves to only three smoothly shaped vowel regions in the F 1 -F 2 diagram, see Fig. 2. We choose these regions to be wellseparated. By this we only consider vowels that are very distinct. The O-group contains vowels like /o/ and /u/, the A-group vowels like /a/ and /æ/, and the I-group vowels near /i/. Our specific choice for these three regions was based on our intuition, but is justified also by the results in (Table 1) of [7]. In this paper the authors have performed a neural network classification of vowels from reflection coefficients. Their results show classification confusions between vowels, reducing their classification accuracy to only three groups. These three groups are similar to our groups. In the next section we link the three vowel groups to distinct video lip shape parameters to improve our detector. Figure 2. Speech vowels in the F 1 -F 2 plane 3. VIDEO LIP SIGNALS AND DETECTION 3.1. Lip Finding and Contour Tracking We want to extract the vertical mouth opening m and the distance w between mouth corners as indicated in the lip contour model of Fig. 3a. First, we locate the faces in an image with a face detection algorithm based on [8]. Then, for each face, we select the mouth region-of-interest (MROI) as the lower part of the face region. An example MROI is shown in Fig. 3b. Figure 3. (a) Lip contour model; (b) Mouth region of interest (MROI) and search lines for lip edges. The locations of the mouth corners are extracted as follows. First, a binary image is calculated by dynamic thresholding of the MROI. We then look for the blob in the binary image which has the most mouth-like shape. Finally, the locations of the mouth corners are found as the left and right extremities of the mouth blob. The edges of the lips are found on search lines perpendicular to the line between the mouth corners (Fig. 3b). On the q-th search line we apply a function to the p-th pixel value such that a number R is obtained which is large for the red lip area and small for skin, teeth and the inner mouth. Then, for each pixel on the q-th line the value of R is compared to a threshold in order to yield the four edges of the two lips it crosses. Finally, two internal lip edges defining m are found as a second-order polynomial fit on the mouth corners and lip edge points, excluding outliers by a median operation EURASIP 2391

3 We calculate R as R(q, p) = Q(q, p) {max(y (q, p) φ hi (q), 0) + max( Y (q, p) + φ lo (q), 0)}. (3) Here the number Q(q, p) is a mapping of the chroma values C b and C r according to Q(q, p) = α 1 Cr(q, p) + α 2 Cb(q, p), where α 1 and α 2 are chosen to favor the reddish color of the lips. We used α 1 = 0.88 and α 2 = The second term in Eq. 3 is the correction for luminance Y on Q resulting in R becoming small for pixels that belong to the (bright) teeth or the (dark) mouth opening. The threshold φ hi (q) is calculated as the average luminance µ Y (q) of the q-th line. The threshold φ lo (q) is chosen to be selective only for the darkest pixels, and is calculated as φ lo (q) = µ Y (q) 0.8σ Y (q) where σ Y (q) is the standard deviation of Y (q) Video Speech Activity Detection When speech activity is detected from the audio modality, we exclude activity of some people in the image by inspecting their video lip activity. The video detector must be conservative for silence detection, meaning when silence is detected it is quite certain that this is true. For visual speech detection we follow an approach like in [3]. Let t denote the index of a video frame. We detect speech activity from the video when ṽ[t] > θ v (4) and speech silence otherwise, with θ v a small fixed positive threshold that we obtained experimentally. Here ṽ[t] is the time-smoothed version of the vertical mouth velocity v[t] according to with ṽ[t] = α ṽ[t 1] + (1 α)v[t] (5) v[t] = m n [t] m n [t 1], m n [t] = m[t]/µ w, and µ w the average horizontal mouth opening in number of pixels serving the purpose of normalizing m[t]. Unlike [3] we use an asymmetric recursion in Eq. 5 with fast (rise) response favoring situations when the mouth is opening and slow (decay) response when the mouth is closing. In this way the detector is conservative for detection of silence. We also apply a fast response when the vertical mouth opening is completely zero. To achieve this α is given by { αf when m α = n [t] > m n [t 1] or m n [t] = 0, α s when m n [t] m n [t 1] and m n [t] 0. Here α f = τ f F v 1 τ f F v and α s = τ sf v 1 τ s F v, with F v the video frame rate and we choose τ f = 1/16s and τ s = 1/8s. A combined audiovisual speech detector for each person in the image is now obtained by multiplying the audio-only detection result from Section 2.1 with the video-only detection result belonging to that person from Eq. 4. With the addition of the video-only detector some people can be excluded when the audio modality has detected speech activity. It is not a sufficient measure however; there remains ambiguity when people move their lips without actually speaking (e.g. when they smile). We will show in the next section that some ambiguous detections can be eliminated by correlating detected audio formant frequencies with video lip shape parameters, which is the main contribution of this paper. 4. AUDIOVISUAL DETECTION 4.1. Lips and Vowel Groups In the F 1 -F 2 diagram of Fig. 2 we distinguished three vowel groups. Next, we relate these three vowel groups to typical mouth shapes. The vowel height is a feature expressing the vertical position of the tongue relative to the roof of the mouth during vowels sounds. Likewise, the vowel backness expresses the horizontal tongue position relative to the back of the mouth. In [5] vowels are related to F 1 and F 2, and in the International Phonetic Alphabet (IPA) chart vowels are related to vowel height and backness. More specifically, it can be deduced from [5] and the IPA chart that the first formant frequency F 1 is related to vowel height, and the second formant frequency F 2 is related to vowel backness. A low vowel from the A-group has a high F 1, and a high vowel from the O- group or the I-group has a low F 2. Back vowels from the O-group have a low F 2, and front vowels from the I-group have a high F 2. From video we cannot measure tongue positions, only lip shapes, but from the literature and our own experience we learned that there is a correlation between vowel backness (hence F 2 ) and roundedness of a lip shape. Also, we noticed from experiments a phonetic correlation between vowel height (hence F 1 ) and vertical mouth opening. The mentioned experiments involved the visual inspection of recorded lip images of persons which were pronouncing different isolated vowels. We selected from these experiments a representative lip shape in each of the three audio vowel groups (Fig. 4) Detection Fig. 5 shows for two alternately-talking people the results of the audiovisual activity detector from Section 3.2, which is achieved by multiplying the result of the audio-only detector with the video-only mouth activity detector. The figure shows ambiguous detections. For example, in the interval t {19.0,, 21.0} only person 1 was talking, but the detector incorrectly finds speech for person 2 that momentarily 2007 EURASIP 2392

4 Figure 5. Audiovisual speech detection for two persons Figure 6. Audiovisual vowel detection moved the lips without producing sound. Using Fig. 4 we can remove some ambiguity when we find a clear visual support from the lip shape of only one person (and no other) for the detected formant frequencies. In this article we focus on the visual detection of roundedness because it proved to be the strongest cue. Roundedness is detected when m w > θ r and w < θ w µ w, (6) where θ r = 0.2 and θ w = 0.8 proved to give conservative results. When we detect from F 1 and F 2 that the current sound stems from the /o/-group, and when we detect roundedness for only one person according to Eq. 6, then we set the activity detection result to false for the other persons for as long as there is ambiguity. In Fig. 6 we plot the video lip parameters for both persons, and the detected vowels from F 1 and F 2. As shown in the vowel plot, at t = 19.6 a clear vowel from the /o/-group is recognized. As can be derived from the two roundedness plots and from Eq. 6 this vowel is visually supported by the mouth shape of person 1 and not by the mouth shape of person EURASIP 2393

5 Figure 7. Improved audiovisual speech detection Figure 4. Distinct mouth shapes in vowel groups [3] D. Sodoyer, B. Rivet, L Girin, J.-L. Schwartz, and C. Jutten, An analysis of visual speech information applied to voice activity detection, in Proceedings ICASSP. IEEE, 2006, vol. I, pp [4] R. Martin, Noise power spectral density estimation based on optimal smoothing and minimum statistics, IEEE Trans. Speech Audio Processing, vol. 9(5), pp , July [5] G.E. Peterson and H.L. Barney, Control methods used in a study of the vowels, Journal of the Acoustical Society of America, vol. 24(2), pp , Mar [6] S. Kay, Modern Spectral Estimation, Prentice-Hall, [7] S. Kshirsagar and M. Magnenat-Thalmann, Lip synchronization using linear predictive analysis, in IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2000, vol. 2, pp [8] P. Viola and M.J. Jones, Robust real-time face detection, International Journal of Computer Vision, vol. 57, no. 2, pp , From this observation we can remove ambiguity by setting the detection result for person 2 to false immediately after t = 19.6 until the moment that there is no longer ambiguity. The resulting improved detection for the second person is shown in Fig CONCLUSIONS We have given an audiovisual approach to speech activity detection for systems with one microphone and one camera, and with multiple persons in the camera s field of view. From an audio-only detector it is not clear which person talks. Combination with a video lip activity detector helps, but still leaves ambiguity when someone moves the lips without talking. We introduced a formant diagram in which we distinguished three separated vowel groups that can be linked with video lip shape parameters. We showed that this diagram is a useful tool to remove ambiguous detections and provide more clarity about which person talks. 6. REFERENCES [1] P.L. Chu, Voice-Activated AGC for Teleconferencing, in Proceedings ICASSP. IEEE, 1996, pp [2] P. Liu and Z. Want, Voice Activity Detection Using Visual Information, in Proceedings ICASSP. IEEE, 2004, vol. I, pp EURASIP 2394

Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm

Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm Prof. Ch.Srinivasa Kumar Prof. and Head of department. Electronics and communication Nalanda Institute

More information

Human Emotion Recognition From Speech

Human Emotion Recognition From Speech RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati

More information

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,

More information

Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction

Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction INTERSPEECH 2015 Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction Akihiro Abe, Kazumasa Yamamoto, Seiichi Nakagawa Department of Computer

More information

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Sanket S. Kalamkar and Adrish Banerjee Department of Electrical Engineering

More information

WHEN THERE IS A mismatch between the acoustic

WHEN THERE IS A mismatch between the acoustic 808 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 3, MAY 2006 Optimization of Temporal Filters for Constructing Robust Features in Speech Recognition Jeih-Weih Hung, Member,

More information

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines Amit Juneja and Carol Espy-Wilson Department of Electrical and Computer Engineering University of Maryland,

More information

Eli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano. Graduate School of Information Science, Nara Institute of Science & Technology

Eli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano. Graduate School of Information Science, Nara Institute of Science & Technology ISCA Archive SUBJECTIVE EVALUATION FOR HMM-BASED SPEECH-TO-LIP MOVEMENT SYNTHESIS Eli Yamamoto, Satoshi Nakamura, Kiyohiro Shikano Graduate School of Information Science, Nara Institute of Science & Technology

More information

International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012

International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012 Text-independent Mono and Cross-lingual Speaker Identification with the Constraint of Limited Data Nagaraja B G and H S Jayanna Department of Information Science and Engineering Siddaganga Institute of

More information

Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition

Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition Hua Zhang, Yun Tang, Wenju Liu and Bo Xu National Laboratory of Pattern Recognition Institute of Automation, Chinese

More information

Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition Using Support Vector Machine Speech Emotion Recognition Using Support Vector Machine Yixiong Pan, Peipei Shen and Liping Shen Department of Computer Technology Shanghai JiaoTong University, Shanghai, China panyixiong@sjtu.edu.cn,

More information

Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape

Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Lip reading: Japanese vowel recognition by tracking temporal changes of lip shape Koshi Odagiri 1, and Yoichi Muraoka 1 1 Graduate School of Fundamental/Computer Science and Engineering, Waseda University,

More information

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project Phonetic- and Speaker-Discriminant Features for Speaker Recognition by Lara Stoll Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California

More information

Speech Synthesis in Noisy Environment by Enhancing Strength of Excitation and Formant Prominence

Speech Synthesis in Noisy Environment by Enhancing Strength of Excitation and Formant Prominence INTERSPEECH September,, San Francisco, USA Speech Synthesis in Noisy Environment by Enhancing Strength of Excitation and Formant Prominence Bidisha Sharma and S. R. Mahadeva Prasanna Department of Electronics

More information

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS

AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic

More information

Speaker recognition using universal background model on YOHO database

Speaker recognition using universal background model on YOHO database Aalborg University Master Thesis project Speaker recognition using universal background model on YOHO database Author: Alexandre Majetniak Supervisor: Zheng-Hua Tan May 31, 2011 The Faculties of Engineering,

More information

Proceedings of Meetings on Acoustics

Proceedings of Meetings on Acoustics Proceedings of Meetings on Acoustics Volume 19, 2013 http://acousticalsociety.org/ ICA 2013 Montreal Montreal, Canada 2-7 June 2013 Speech Communication Session 2aSC: Linking Perception and Production

More information

Learning Methods in Multilingual Speech Recognition

Learning Methods in Multilingual Speech Recognition Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex

More information

Segregation of Unvoiced Speech from Nonspeech Interference

Segregation of Unvoiced Speech from Nonspeech Interference Technical Report OSU-CISRC-8/7-TR63 Department of Computer Science and Engineering The Ohio State University Columbus, OH 4321-1277 FTP site: ftp.cse.ohio-state.edu Login: anonymous Directory: pub/tech-report/27

More information

Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean

More information

Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers

Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers October 31, 2003 Amit Juneja Department of Electrical and Computer Engineering University of Maryland, College Park,

More information

Speaker Identification by Comparison of Smart Methods. Abstract

Speaker Identification by Comparison of Smart Methods. Abstract Journal of mathematics and computer science 10 (2014), 61-71 Speaker Identification by Comparison of Smart Methods Ali Mahdavi Meimand Amin Asadi Majid Mohamadi Department of Electrical Department of Computer

More information

1. REFLEXES: Ask questions about coughing, swallowing, of water as fast as possible (note! Not suitable for all

1. REFLEXES: Ask questions about coughing, swallowing, of water as fast as possible (note! Not suitable for all Human Communication Science Chandler House, 2 Wakefield Street London WC1N 1PF http://www.hcs.ucl.ac.uk/ ACOUSTICS OF SPEECH INTELLIGIBILITY IN DYSARTHRIA EUROPEAN MASTER S S IN CLINICAL LINGUISTICS UNIVERSITY

More information

Mandarin Lexical Tone Recognition: The Gating Paradigm

Mandarin Lexical Tone Recognition: The Gating Paradigm Kansas Working Papers in Linguistics, Vol. 0 (008), p. 8 Abstract Mandarin Lexical Tone Recognition: The Gating Paradigm Yuwen Lai and Jie Zhang University of Kansas Research on spoken word recognition

More information

IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 3, MARCH

IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 3, MARCH IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 17, NO. 3, MARCH 2009 423 Adaptive Multimodal Fusion by Uncertainty Compensation With Application to Audiovisual Speech Recognition George

More information

Self-Supervised Acquisition of Vowels in American English

Self-Supervised Acquisition of Vowels in American English Self-Supervised Acquisition of Vowels in American English Michael H. Coen MIT Computer Science and Artificial Intelligence Laboratory 32 Vassar Street Cambridge, MA 2139 mhcoen@csail.mit.edu Abstract This

More information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE

Edexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional

More information

A comparison of spectral smoothing methods for segment concatenation based speech synthesis

A comparison of spectral smoothing methods for segment concatenation based speech synthesis D.T. Chappell, J.H.L. Hansen, "Spectral Smoothing for Speech Segment Concatenation, Speech Communication, Volume 36, Issues 3-4, March 2002, Pages 343-373. A comparison of spectral smoothing methods for

More information

OCR for Arabic using SIFT Descriptors With Online Failure Prediction

OCR for Arabic using SIFT Descriptors With Online Failure Prediction OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,

More information

Statewide Framework Document for:

Statewide Framework Document for: Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance

More information

A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language

A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language Z.HACHKAR 1,3, A. FARCHI 2, B.MOUNIR 1, J. EL ABBADI 3 1 Ecole Supérieure de Technologie, Safi, Morocco. zhachkar2000@yahoo.fr.

More information

Speaker Recognition. Speaker Diarization and Identification

Speaker Recognition. Speaker Diarization and Identification Speaker Recognition Speaker Diarization and Identification A dissertation submitted to the University of Manchester for the degree of Master of Science in the Faculty of Engineering and Physical Sciences

More information

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 2, Ver.1 (Mar - Apr.2015), PP 55-61 www.iosrjournals.org Analysis of Emotion

More information

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford

More information

Voice conversion through vector quantization

Voice conversion through vector quantization J. Acoust. Soc. Jpn.(E)11, 2 (1990) Voice conversion through vector quantization Masanobu Abe, Satoshi Nakamura, Kiyohiro Shikano, and Hisao Kuwabara A TR Interpreting Telephony Research Laboratories,

More information

Body-Conducted Speech Recognition and its Application to Speech Support System

Body-Conducted Speech Recognition and its Application to Speech Support System Body-Conducted Speech Recognition and its Application to Speech Support System 4 Shunsuke Ishimitsu Hiroshima City University Japan 1. Introduction In recent years, speech recognition systems have been

More information

How to Judge the Quality of an Objective Classroom Test

How to Judge the Quality of an Objective Classroom Test How to Judge the Quality of an Objective Classroom Test Technical Bulletin #6 Evaluation and Examination Service The University of Iowa (319) 335-0356 HOW TO JUDGE THE QUALITY OF AN OBJECTIVE CLASSROOM

More information

PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES

PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES PREDICTING SPEECH RECOGNITION CONFIDENCE USING DEEP LEARNING WITH WORD IDENTITY AND SCORE FEATURES Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, Li Deng Department of Electrical and Computer Engineering,

More information

Why Did My Detector Do That?!

Why Did My Detector Do That?! Why Did My Detector Do That?! Predicting Keystroke-Dynamics Error Rates Kevin Killourhy and Roy Maxion Dependable Systems Laboratory Computer Science Department Carnegie Mellon University 5000 Forbes Ave,

More information

GCSE Mathematics B (Linear) Mark Scheme for November Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education

GCSE Mathematics B (Linear) Mark Scheme for November Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education GCSE Mathematics B (Linear) Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education Mark Scheme for November 2014 Oxford Cambridge and RSA Examinations OCR (Oxford Cambridge

More information

Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition

Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition Seltzer, M.L.; Raj, B.; Stern, R.M. TR2004-088 December 2004 Abstract

More information

Noise-Adaptive Perceptual Weighting in the AMR-WB Encoder for Increased Speech Loudness in Adverse Far-End Noise Conditions

Noise-Adaptive Perceptual Weighting in the AMR-WB Encoder for Increased Speech Loudness in Adverse Far-End Noise Conditions 26 24th European Signal Processing Conference (EUSIPCO) Noise-Adaptive Perceptual Weighting in the AMR-WB Encoder for Increased Speech Loudness in Adverse Far-End Noise Conditions Emma Jokinen Department

More information

A study of speaker adaptation for DNN-based speech synthesis

A study of speaker adaptation for DNN-based speech synthesis A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu, Pawel Swietojanski, Christophe Veaux, Steve Renals, Simon King The Centre for Speech Technology Research (CSTR) University of Edinburgh,

More information

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,

More information

Visit us at:

Visit us at: White Paper Integrating Six Sigma and Software Testing Process for Removal of Wastage & Optimizing Resource Utilization 24 October 2013 With resources working for extended hours and in a pressurized environment,

More information

Self-Supervised Acquisition of Vowels in American English

Self-Supervised Acquisition of Vowels in American English Self-Supervised cquisition of Vowels in merican English Michael H. Coen MIT Computer Science and rtificial Intelligence Laboratory 32 Vassar Street Cambridge, M 2139 mhcoen@csail.mit.edu bstract This paper

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

BODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY

BODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY BODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY Sergey Levine Principal Adviser: Vladlen Koltun Secondary Adviser:

More information

Acoustic correlates of stress and their use in diagnosing syllable fusion in Tongan. James White & Marc Garellek UCLA

Acoustic correlates of stress and their use in diagnosing syllable fusion in Tongan. James White & Marc Garellek UCLA Acoustic correlates of stress and their use in diagnosing syllable fusion in Tongan James White & Marc Garellek UCLA 1 Introduction Goals: To determine the acoustic correlates of primary and secondary

More information

AP Statistics Summer Assignment 17-18

AP Statistics Summer Assignment 17-18 AP Statistics Summer Assignment 17-18 Welcome to AP Statistics. This course will be unlike any other math class you have ever taken before! Before taking this course you will need to be competent in basic

More information

Introduction to the Practice of Statistics

Introduction to the Practice of Statistics Chapter 1: Looking at Data Distributions Introduction to the Practice of Statistics Sixth Edition David S. Moore George P. McCabe Bruce A. Craig Statistics is the science of collecting, organizing and

More information

Revisiting the role of prosody in early language acquisition. Megha Sundara UCLA Phonetics Lab

Revisiting the role of prosody in early language acquisition. Megha Sundara UCLA Phonetics Lab Revisiting the role of prosody in early language acquisition Megha Sundara UCLA Phonetics Lab Outline Part I: Intonation has a role in language discrimination Part II: Do English-learning infants have

More information

STA 225: Introductory Statistics (CT)

STA 225: Introductory Statistics (CT) Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic

More information

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview

Algebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best

More information

Circuit Simulators: A Revolutionary E-Learning Platform

Circuit Simulators: A Revolutionary E-Learning Platform Circuit Simulators: A Revolutionary E-Learning Platform Mahi Itagi Padre Conceicao College of Engineering, Verna, Goa, India. itagimahi@gmail.com Akhil Deshpande Gogte Institute of Technology, Udyambag,

More information

College Pricing and Income Inequality

College Pricing and Income Inequality College Pricing and Income Inequality Zhifeng Cai U of Minnesota, Rutgers University, and FRB Minneapolis Jonathan Heathcote FRB Minneapolis NBER Income Distribution, July 20, 2017 The views expressed

More information

Automatic Pronunciation Checker

Automatic Pronunciation Checker Institut für Technische Informatik und Kommunikationsnetze Eidgenössische Technische Hochschule Zürich Swiss Federal Institute of Technology Zurich Ecole polytechnique fédérale de Zurich Politecnico federale

More information

Automatic segmentation of continuous speech using minimum phase group delay functions

Automatic segmentation of continuous speech using minimum phase group delay functions Speech Communication 42 (24) 429 446 www.elsevier.com/locate/specom Automatic segmentation of continuous speech using minimum phase group delay functions V. Kamakshi Prasad, T. Nagarajan *, Hema A. Murthy

More information

Linking the Ohio State Assessments to NWEA MAP Growth Tests *

Linking the Ohio State Assessments to NWEA MAP Growth Tests * Linking the Ohio State Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. August 2016 Introduction Northwest Evaluation Association (NWEA

More information

Quarterly Progress and Status Report. VCV-sequencies in a preliminary text-to-speech system for female speech

Quarterly Progress and Status Report. VCV-sequencies in a preliminary text-to-speech system for female speech Dept. for Speech, Music and Hearing Quarterly Progress and Status Report VCV-sequencies in a preliminary text-to-speech system for female speech Karlsson, I. and Neovius, L. journal: STL-QPSR volume: 35

More information

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration INTERSPEECH 2013 Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration Yan Huang, Dong Yu, Yifan Gong, and Chaojun Liu Microsoft Corporation, One

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

The NICT/ATR speech synthesis system for the Blizzard Challenge 2008

The NICT/ATR speech synthesis system for the Blizzard Challenge 2008 The NICT/ATR speech synthesis system for the Blizzard Challenge 2008 Ranniery Maia 1,2, Jinfu Ni 1,2, Shinsuke Sakai 1,2, Tomoki Toda 1,3, Keiichi Tokuda 1,4 Tohru Shimizu 1,2, Satoshi Nakamura 1,2 1 National

More information

Perceptual scaling of voice identity: common dimensions for different vowels and speakers

Perceptual scaling of voice identity: common dimensions for different vowels and speakers DOI 10.1007/s00426-008-0185-z ORIGINAL ARTICLE Perceptual scaling of voice identity: common dimensions for different vowels and speakers Oliver Baumann Æ Pascal Belin Received: 15 February 2008 / Accepted:

More information

Speech Recognition at ICSI: Broadcast News and beyond

Speech Recognition at ICSI: Broadcast News and beyond Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI

More information

learning collegiate assessment]

learning collegiate assessment] [ collegiate learning assessment] INSTITUTIONAL REPORT 2005 2006 Kalamazoo College council for aid to education 215 lexington avenue floor 21 new york new york 10016-6023 p 212.217.0700 f 212.661.9766

More information

Grade 6: Correlated to AGS Basic Math Skills

Grade 6: Correlated to AGS Basic Math Skills Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and

More information

CONSTRUCTION OF AN ACHIEVEMENT TEST Introduction One of the important duties of a teacher is to observe the student in the classroom, laboratory and

CONSTRUCTION OF AN ACHIEVEMENT TEST Introduction One of the important duties of a teacher is to observe the student in the classroom, laboratory and CONSTRUCTION OF AN ACHIEVEMENT TEST Introduction One of the important duties of a teacher is to observe the student in the classroom, laboratory and in other settings. He may also make use of tests in

More information

Digital Signal Processing: Speaker Recognition Final Report (Complete Version)

Digital Signal Processing: Speaker Recognition Final Report (Complete Version) Digital Signal Processing: Speaker Recognition Final Report (Complete Version) Xinyu Zhou, Yuxin Wu, and Tiezheng Li Tsinghua University Contents 1 Introduction 1 2 Algorithms 2 2.1 VAD..................................................

More information

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION Mitchell McLaren 1, Yun Lei 1, Luciana Ferrer 2 1 Speech Technology and Research Laboratory, SRI International, California, USA 2 Departamento

More information

Reducing Features to Improve Bug Prediction

Reducing Features to Improve Bug Prediction Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science

More information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule Learning With Negation: Issues Regarding Effectiveness Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United

More information

Author's personal copy

Author's personal copy Speech Communication 49 (2007) 588 601 www.elsevier.com/locate/specom Abstract Subjective comparison and evaluation of speech enhancement Yi Hu, Philipos C. Loizou * Department of Electrical Engineering,

More information

Using EEG to Improve Massive Open Online Courses Feedback Interaction

Using EEG to Improve Massive Open Online Courses Feedback Interaction Using EEG to Improve Massive Open Online Courses Feedback Interaction Haohan Wang, Yiwei Li, Xiaobo Hu, Yucong Yang, Zhu Meng, Kai-min Chang Language Technologies Institute School of Computer Science Carnegie

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Functional Skills Mathematics Level 2 assessment

Functional Skills Mathematics Level 2 assessment Functional Skills Mathematics Level 2 assessment www.cityandguilds.com September 2015 Version 1.0 Marking scheme ONLINE V2 Level 2 Sample Paper 4 Mark Represent Analyse Interpret Open Fixed S1Q1 3 3 0

More information

Calibration of Confidence Measures in Speech Recognition

Calibration of Confidence Measures in Speech Recognition Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE

More information

On-Line Data Analytics

On-Line Data Analytics International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob

More information

INPE São José dos Campos

INPE São José dos Campos INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA

More information

Affective Classification of Generic Audio Clips using Regression Models

Affective Classification of Generic Audio Clips using Regression Models Affective Classification of Generic Audio Clips using Regression Models Nikolaos Malandrakis 1, Shiva Sundaram, Alexandros Potamianos 3 1 Signal Analysis and Interpretation Laboratory (SAIL), USC, Los

More information

The Good Judgment Project: A large scale test of different methods of combining expert predictions

The Good Judgment Project: A large scale test of different methods of combining expert predictions The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania

More information

Dublin City Schools Mathematics Graded Course of Study GRADE 4

Dublin City Schools Mathematics Graded Course of Study GRADE 4 I. Content Standard: Number, Number Sense and Operations Standard Students demonstrate number sense, including an understanding of number systems and reasonable estimates using paper and pencil, technology-supported

More information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX

More information

UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak

UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS. Heiga Zen, Haşim Sak UNIDIRECTIONAL LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK WITH RECURRENT OUTPUT LAYER FOR LOW-LATENCY SPEECH SYNTHESIS Heiga Zen, Haşim Sak Google fheigazen,hasimg@google.com ABSTRACT Long short-term

More information

On the Combined Behavior of Autonomous Resource Management Agents

On the Combined Behavior of Autonomous Resource Management Agents On the Combined Behavior of Autonomous Resource Management Agents Siri Fagernes 1 and Alva L. Couch 2 1 Faculty of Engineering Oslo University College Oslo, Norway siri.fagernes@iu.hio.no 2 Computer Science

More information

VIEW: An Assessment of Problem Solving Style

VIEW: An Assessment of Problem Solving Style 1 VIEW: An Assessment of Problem Solving Style Edwin C. Selby, Donald J. Treffinger, Scott G. Isaksen, and Kenneth Lauer This document is a working paper, the purposes of which are to describe the three

More information

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS. Elliot Singer and Douglas Reynolds

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS. Elliot Singer and Douglas Reynolds DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS Elliot Singer and Douglas Reynolds Massachusetts Institute of Technology Lincoln Laboratory {es,dar}@ll.mit.edu ABSTRACT

More information

Voiceless Stop Consonant Modelling and Synthesis Framework Based on MISO Dynamic System

Voiceless Stop Consonant Modelling and Synthesis Framework Based on MISO Dynamic System ARCHIVES OF ACOUSTICS Vol. 42, No. 3, pp. 375 383 (2017) Copyright c 2017 by PAN IPPT DOI: 10.1515/aoa-2017-0039 Voiceless Stop Consonant Modelling and Synthesis Framework Based on MISO Dynamic System

More information

Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty

Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty Julie Medero and Mari Ostendorf Electrical Engineering Department University of Washington Seattle, WA 98195 USA {jmedero,ostendor}@uw.edu

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

FOR TEACHERS ONLY. The University of the State of New York REGENTS HIGH SCHOOL EXAMINATION PHYSICAL SETTING/PHYSICS

FOR TEACHERS ONLY. The University of the State of New York REGENTS HIGH SCHOOL EXAMINATION PHYSICAL SETTING/PHYSICS PS P FOR TEACHERS ONLY The University of the State of New York REGENTS HIGH SCHOOL EXAMINATION PHYSICAL SETTING/PHYSICS Thursday, June 21, 2007 9:15 a.m. to 12:15 p.m., only SCORING KEY AND RATING GUIDE

More information

Generative models and adversarial training

Generative models and adversarial training Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Ch 2 Test Remediation Work Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) High temperatures in a certain

More information

College Pricing and Income Inequality

College Pricing and Income Inequality College Pricing and Income Inequality Zhifeng Cai U of Minnesota and FRB Minneapolis Jonathan Heathcote FRB Minneapolis OSU, November 15 2016 The views expressed herein are those of the authors and not

More information

COMPUTER INTERFACES FOR TEACHING THE NINTENDO GENERATION

COMPUTER INTERFACES FOR TEACHING THE NINTENDO GENERATION Session 3532 COMPUTER INTERFACES FOR TEACHING THE NINTENDO GENERATION Thad B. Welch, Brian Jenkins Department of Electrical Engineering U.S. Naval Academy, MD Cameron H. G. Wright Department of Electrical

More information