CROSS LINGUAL MODELLING EXPERIMENTS FOR INDONESIAN
|
|
- Rosamund Simpson
- 5 years ago
- Views:
Transcription
1 CROSS LINGUAL MODELLING EXPERIMENTS FOR INDONESIAN Terrence Martin, Sridha Sridharan Speech Research Laboratory, RCSAVT, School of Electrical and Electronic Systems Engineering Queensland University of Technology GPO Box 2434, George St, BRISBANE, Australia, (tl.martin, ABSTRACT: The extension of Large Vocabulary Continuous Speech Recognition (LVCSR) to resource poor languages such as Indonesian is hindered by the lack of transcribed acoustic data and appropriate pronunciation lexicons. Research has generally been directed toward establishing robust cross-lingual acoustic models, with the assumption that phonetic lexicons are readily available. This is not the case for Indonesian. This paper outlines the development of a small Indonesian lexicon and the transcription of Indonesian speech taken from the OGI Multi Language speech corpus. To overcome the lack of transcribed acoustic data, previous research has indicated that acoustic data from data rich languages can be used to train phoneme models for subsequent use in recognition of a target language. Using current cross lingual speech recognition techniques, the goal of this paper is to outline our preliminary experiments aimed at establishing which source languages are most suitable for use in Indonesian speech recognition. We investigate cross language transfer using English, Spanish and Hindi speech to train our acoustic source models for both phoneme and word recognition. A comparison between knowledge based and data driven mapping techniques and their effects on recognition is also tabled. It was found that Hindi speech models gave significantly improved recognition performance in comparison to English and Spanish. 1. INTRODUCTION Bahasa Indonesia and Bahasa Melayu, literally the Indonesian and Malay language respectively, constitute the national language for Malaysia, Indonesia, Singapore and Brunei. It is the most common form of inter-ethnic communication for approximately 200 million people in business, education and government. Unfortunately, the extension of Large Vocabulary Continuous Speech Recognition (LVCSR) technology to so called minority languages such as Indonesian is hindered by a lack of appropriate resources. While Indonesian may be poorly provided for in terms of resources for speech recognition, it still arguably ranks within the 20 most important languages in the world. In fact, after taking into consideration variability, distribution and importance, Schultz and Waibel (1997) rate Malay/Indonesian within the top ten languages. This disparity between the relative importance of the Indonesian language and the availability of speech recognition resources highlights the requirements for further research into the application of speech technology to Indonesian. Recent research (Beyerlein et al., 1999a; Schultz and Waibel, 2001b; Kohler, 1998) has indicated that a promising method for overcoming the lack of acoustic resources in the target language is by using acoustic models trained in data rich source languages for subsequent use in recognition in the target language. If sufficient target language acoustic data is available, adaptation can be performed on the source models so they are more representative of the feature space for the target language. Our research focus is on extracting suitable models from data rich languages such as English, Spanish and Hindi and adapting them to produce a state-of-the-art Indonesian speech recognition system. In (Schultz and Waibel, 2001b), it was shown that the accuracy achieved by implementing cross-language phone models in Automatic Speech Recognition (ASR) can be significantly affected by the selected source languages. The acquisition of the resources required to build a speech recognition system is expensiveandthesubsequenttailoringofthesemodelstomakethemsuitableforuseonatargetlangauge is time consuming. To minimise cost and time considerations we are conducting preliminary experiments Proceedings of the 9th Australian International Conference on Speech Science &Technology Melb Dec 2to5-Accepted after abstract review Australian c Speech Science & Technology Association Inc. Page 184
2 focused on establishing which languages are most suitable for an Indonesian speech recognition system. To achieve this, we examined the coverage and performance of English, Hindi, and Spanish for conducting cross language acoustic speech recognition on Indonesian. An acoustic corpus is only one of the resources required for statistical ASR; text corpora for language models and pronunciation lexicons are also required. However, most studies have relied on the availability of these resources. Unfortunately, to the authors knowledge, there are no pronunciation lexicons available. Currently, a 15k word commercial dictionary is being prepared for our research. In the interim we have developed a small (800 word) pronunciation lexicon for testing purposes. 2. ACOUSTIC DATABASE CREATION Phonetically transcribed acoustic data for Spanish, English, and Hindi was taken from the 22 Language Oregon Graduate Institute Multi Language Telephone Speech Corpus and used to train the acoustic models for the source languages. The source language speech is transcribed using Worldbet notation (Hieronymus, 1993), and is phonetically segmented and time aligned. Worldbet is an ASCII based character set encoding of the International Phonetic Alphabet (IPA). Table 1 outlines the total utterance durations and compositional details. Table 1: Cross Lingual Acoustic Source Data Specifications Language 1 Male Speakers 2 Female Speakers 3 Time(hr) English Spanish Hindi The choice of the OGI database and more specifically, the English, Spanish and Hindi languages enabled us to obtain adequate, if somewhat limited, data for training cross lingual acoustic models from a similar environment. Other languages such as Mandarin, were not utilised in this preliminary study because of the additional complications involved with tonal languages, ideographic scripts, or syllable based languages (Schultz and Waibel, 2001b). Using data recorded in a similar environment provided the opportunity to standardise the training and test environment and hopefully reduce the impact of train/test mismatch and variations in channel effects. To provide Indonesian acoustic test data, the entire OGI Indonesian acoustic corpus was transcribed and validated at the word level by two native Indonesian speakers. From this data, a subset comprising 22 of the 1 minute stories before the tone were selected for subsequent phonetic transcription. The transcription was then independently validated and non-speech artifacts inserted in accordance with the Worldbet notation. All instances of non-speech artifacts, in both the source and target transcriptions, were subsequently mapped to a single non-speech noise model for this series of experiments. 3. DICTIONARY CREATION To produce a phonetic transcript of the Indonesian speech, a phonetic lexicon was manufactured using the basic letter to sound rules taken from the Kamus Indonesia Inggris (Echols and Hassan, 1990), and cross checked with the rudimentary Indonesian pronunciations found in the The Learners Dictionary of Todays Indonesian (Quinn, 2001). The Kamus Indonesia Inggris dictionary contains an IPA based representation for the Indonesian phoneme set and basic letter to sound rules for pronunciation. We mapped the IPA set to Worldbet notation using Hieronymus (1993). The pronunciation lexicon created using these rules was checked using (Quinn, 2001) and then finally validated by cross-referencing against acoustic evidence. This resulted in the addition of several non-native phonemes to provide pronunciation coverage for the non-native words like Oregon which appear frequently in the Indonesian speech corpus and also to allow for additional within language variations. The entire lexicon consisted of nearly 800 words. This dictionary served as an interim measure for our preliminary testing until the completion of a 15k dictionary, which is being commercially produced. Proceedings of the 9th Australian International Conference on Speech Science &Technology Melbourne,December 2 to 5,2002 Accepted after abstract review c Australian Speech Science & Technology Association Inc. Page 185
3 4. CROSS LANGUAGE PHONE MAPPING Prior to building the acoustic models, preliminary mapping was required to ensure that the source data provided adequate coverage for the target language phonemes. Two techniques were used to select the best source language acoustic models to represent the target language. Firstly, a knowledge based technique was investigated in which direct mappings were made between equivalent Worldbet phonetic designations in the source and target languages. In the event that a direct mapping was not available, the closest Worldbet counterpart was selected after considering articulatory position and proximity of sound. As highlighted in (Beyerlein et al., 1999b) this technique has the benefit that no target language acoustic data is required. The mapping achieved by exploiting this linguistic knowledge is outlined in Section 4.1. Secondly, a data driven technique was investigated which used confusion matrix data to select the best representative for each target language phone from multiple source languages. This is further discussed in Section 4.2. Experimental results for both techniques are shown in Table 5 of Section Knowledge Driven Phone Mapping Using the OGI transcriptions for the source language and the phone set developed via (Echols and Hassan, 1990) and (Quinn, 2001), we constructed a suitable mapping which is shown in Table 2. The first column indicates the target phone set, including the non-native phonemes ( f, v, z, S, and x ). The subsequent columns show the coverage achieved by each of the three source languages, indicated by an X. For example the first row of phones containing &, b, dz... have X indicated for each language indicated these phones had equivalent Worldbet counterparts in all three source languages. In the event that coverage was not provided by a particular source language, but an allophone with a similar articulatory position and sound was available, then this mapping was substituted. If no representative phoneme was available, a dash is indicated, and in this instance the model created for the universal set was used. Table 2: Cross Lingual Mappings for Indonesian Indonesian Phonemes English Hindi Spanish [ Worldbet ] &,b,dz,e,f,g,i,j X X X k,l,m,n,s,s,ts,u,w X X X,aU,h, > X X -,-, hs,- u, ai,r X u:,ai,rr, X p ph X X d, ei, n, ou,v X d[,e:,n[, o:,- d[, e, n[, o,v &r, 3r X 3 n - - X t th (t[ or tr) t[ It can be seen in Table 2 that each language came close to providing complete phoneme coverage for the target language. The exceptions were that no Spanish source model was available for the vowels, > or the diphthong au. English and Hindi did not have a model for palatal nasal n, and Hindi did not have a model for voiced fricative v which as mentioned, does not appear in native Indonesian. It should be noted that this table only illustrates the target phonemes which had corresponding source language representatives with identical Wordbet/IPA symbols or potential source language replacements when no direct corresponding phoneme was available. There were however, several allophonic variants of the target phoneme available in the source languages. For example, there were several allophones for the trill r (trilled and tapped Spanish version) and plosive t (Hindi hyper-aspirated versions ). Given the rudimentary nature of our dictionary, we felt that these sounds might be more representative of the target language phone features. Accordingly, models for each allophone were created and these were used in the recognition process. After recognition these were mapped back to the base phoneme for comparative purposes. Proceedings of the 9th Australian International Conference on Speech Science &Technology Melb. December 2 to 5,2002 Accepted after abstract review. c Australian Speech Science &Technology Association Inc. Page 186
4 4.2. Automatic Mapping As mentioned earlier, two methods were used to establish suitable source-to-target language mappings. The second technique used the HTK speech recognition toolkit (Young et al., 2001) to compare decoded utterances and the original Indonesian reference phoneme string. Initially we used a global phoneset (containing every phone model from all three source languages) to decode the Indonesian utterances. Unfortunately, this resulted in high confusion rates between dissimilar phonetic categories and produced poor results. To combat this we constrained the mapping choice to only allophonic variants of the target phones, and the recognition rates are shown beside the All 3 entry in Table 5. Deviating from this idea, we used each individual source language phone recogniser to decode the Indonesian utterances, and then compared the resulting confusion matrices to select the best performing allophonic variant. This method is not globally competitive, however, using the phone models selected we were able to obtain a small improvement in performance as shown beside the Individual row in Table 5. These mappings and the corresponding phoneme recognition rates achieved in each source language are shown in Table 3. Table 3: Phone Recognition Rates on Indonesian Target Best Source Target Best Source Target Best Source Phone Phone Phone Phone Phone Phone hi /68% i sp i / 69% &r sp 3 / 69% & sp & / 28% I hi Ix / 49% s hi s / 73% ai sp ai /60% j hi j / 32% S nil recog instances au en au / 42% k hi k/kh / 67% t hi t / 63% b hi b / 58% l sp L / 39% ts sp ts / 63% d hi d(/dr / 55% m hi m / 69% u sp u / 32% dz hi dz / 57% n hi n / 63% U sp U / 31% E sp E / 51% N hi N / 36% v en v / 12% ei sp e / 54% n sp n / 33% w hi w / 36% f en f / 42% ou sp o / 66% g hi gh / 36% p en ph / 69% h hi h / 36% r hi rr/r( / 54% One result to emerge was that Hindi (identified by hi ) provided the best coverage for most Indonesian consonants, with the exception of those consonants taken from loanwords (f, v). For these phones, English ( en ) achieved the best recognition rates. Spanish ( sp ) achieved the best recognition rates for most vowels, with the notable exceptions being au, > and which were not in the Spanish phone set. However, the vowel recognition rate for Hindi on Indonesian vowels achieved only slightly inferior results in most instances Monophone Recognition Results The knowledge-based and Data driven mappings depicted in Table 2 and 3 were used to conduct monophone recognition experiments on Indonesian speech. Speech was parameterised using a 12th order MFCC analysis plus normalised energy, delta and delta-delta features with a frame size/shift of 25/10ms. Cepstral Mean Subtraction (CMS) was carried out to reduce the channel effects. The phone model topology was 3 state left-to-right, with each state emission density comprising 8 Gaussian mixture components. The speech files in the OGI database are sampled at 8 khz and stored using 8 bit, µ-law encoding. A uniform prior phoneme probability was applied for phoneme recognition, that is, a simple phoneme loop recogniser. To give an indicative benchmark for comparative purposes, recognition rates were determined for each source language after decoding a same language test set. The recognition rates are shown in the second column of Table 4. Recognition rates are expressed in terms of percentage correct and percentage accuracy as produced by HTK (Young et al., 2001). Insertion and grammar penalties were adjusted so that the number of insertions and deletions were approximately equal. This acts to normalise the Proceedings of the 9th Australian International Conference on Speech Science &Technology Melb December 2 to Accepted after abstract review. c Australian Speech Science &Technology Association Inc. Page 187
5 speaking rate across all three source languages and provides a more meaningful comparison. A universal model set was also trained across all three languages, based solely on the similarity in Worldbet notation. The result of applying these models to all three source languages is also shown. Table 4: Baseline Source Recognition Rates on Source Test Sets Language % Correct % Accuracy Deletions Insertions Hindi English Spanish Universal The phonetic models developed were then applied to Indonesian speech and the results are shown in Table 5. Table 5: Phone Recognition Rates on Indonesian Language % Correct % Accuracy Deletions Insertions Hindi English Spanish Universal All Individual It can be seen that Hindi significantly outperforms all other source language acoustic models, and examination of the baseline recognition rates in Table 4 reveals that the Hindi recognition of Indonesian performs better than English performance on English test data Word Recognition Results In Schultz and Waibel (2001a) it was found that there was no direct correlation between phoneme recognition rates and subsequent word recognition rates, when multiple languages were applied to the target language. Given this, we wanted to establish whether this idea held when several languages were used for Indonesian speech recognition at the word level. The knowledge based phoneme mappings determined in Section 4.1 were used. No language modelling was incorporated into our system, with a uniform a priori probability applied to each word. Table 6 depicts our results. Table 6: Word Recognition Rates on Indonesian Language % Correct Hindi 16.2 English 8.5 Spanish 10.3 Universal 9.8 Data Driven Set 11.5 As expected, the results were extremely poor, except for the Hindi language which performed surprisingly well. However, Beyerlein et al. (1999a) reported similarly poor WER results when a simple knowledge based mapping was used. The fact that no language modelling was used must also be considered. Proceedings of the 9th Australian International Conference on Speech Science &Technology Melb. December 2 to Accepted after abstract review caustralian Speech Science &Technology Association Inc. Page 188
6 We also applied the mapping derived using confusion matrices outlined in Section 4.2. Unfortunately, whilst the word recognition rates did improve over the English, Universal, and Spanish Models, it failed to better the performance of the Hindi model set. 5. DISCUSSION The interesting result to emerge from these experiments was the superior recognition performance achieved by the Hindi language, at both the monophone and word level, even when compared to data driven methods. Whilst the data driven technique resulted in the selection of a phoneset which achieved individually superior recognition rates, the combination fails to translate to a globally superior solution. The superior recognition rates achieved by the Hindi models may occur because the mixture components in each state provide a more suitable generalisation for the context dependance in Indonesian speech. In addition, the transition between models may be more naturally represented using phonemes from the one language and the general language feature space for Hindi may be closer to Indonesian than the other languages. As seen in Table 3, Hindi and Spanish provided superior consonant and vowel recognition results respectively. This result may be illusory however, given that our data driven technique did not incorporate a more global candidate model selection criteria. Accordingly validation will require further directed research. 6. CONCLUSION In this paper we outlined the transcription of Indonesian speech contained in the 22 Language OGI speech Corpus and the development process for an Indonesian pronunciation dictionary. We examined the recognition performance using two mapping techniques for mapping from source languages to Indonesian. The data driven mapping technique improved recognition rates in comparison to English and Spanish source languages however, Hindi provided a significantly improved performance over all methods. This possibly indicates that the Hindi language provides a better general representation for the Indonesian language. Future examination of context dependance and a language based feature space comparison is required to support the findings in this work. 7. REFERENCES Beyerlein, P., Byrne, B., Huerta, J., Marthi, B., Morgan, J., Pterek, N., Picone, J. and W.Wang (1999a), Towards independant acoustic modelling, Technical report, John Hopkins University. Beyerlein, P., Byrne, B., Huerta, J., Marthi, B., Morgan, J., Pterek, N., Picone, J. and W.Wang (1999b), Towards language independant acoustic modelling,. IEEE Workshop on Automatic Speech Recognition and Understanding,. Echols, J. and Hassan, S. (1990), Kamus Indonesia Inggris, Penerbit PT Grameduia Pustaka Utama. Hieronymus, J. L. (1993), ASCII Phonetic Symbol for the World s Languages : Worldbet, Journal of the International Phonetic Association 23. Kohler, J. (1998), Language adaptation of multilingual phone models for vocabulary independent speech recognition tasks, in Proc. ICASSP 98,, pp Quinn, G. (2001), The Learner s Dictionary of Todays Indonesian, Allen and Unwin. Schultz, T. and Waibel, A. (1997), The Global Phone Project: Multilingual LVCSR with Janus, SQEL, Plzen, pp Schultz, T. and Waibel, A. (2001a), Experiments on cross language acoutic modelling, Proceedings of Eurospeech Schultz, T. and Waibel, A. (2001b), Language independent and language adaptive acoustic modelling, Speech Communication 2001, Vol. 35, pp Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev, V. and Woodland, P. (2001), The HTK Book (for HTK version 3.1), Entropic Ltd. Proceedings of the 9th Australian International Conference on Speech Science &Technology Melb. December 2 to Accepted after abstract review c Australian Speech Science &Technology Association Inc. Page 189
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 informationModeling 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 informationUnvoiced 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 informationModeling 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 informationSTUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH
STUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH Don McAllaster, Larry Gillick, Francesco Scattone, Mike Newman Dragon Systems, Inc. 320 Nevada Street Newton, MA 02160
More informationSpeech 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 informationSpeech 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 informationUniversal contrastive analysis as a learning principle in CAPT
Universal contrastive analysis as a learning principle in CAPT Jacques Koreman, Preben Wik, Olaf Husby, Egil Albertsen Department of Language and Communication Studies, NTNU, Trondheim, Norway jacques.koreman@ntnu.no,
More informationBAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION. Han Shu, I. Lee Hetherington, and James Glass
BAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION Han Shu, I. Lee Hetherington, and James Glass Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge,
More informationSEGMENTAL FEATURES IN SPONTANEOUS AND READ-ALOUD FINNISH
SEGMENTAL FEATURES IN SPONTANEOUS AND READ-ALOUD FINNISH Mietta Lennes Most of the phonetic knowledge that is currently available on spoken Finnish is based on clearly pronounced speech: either readaloud
More informationSemi-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 informationMandarin 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 informationThe 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 informationADVANCES 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 informationA 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 informationConsonants: articulation and transcription
Phonology 1: Handout January 20, 2005 Consonants: articulation and transcription 1 Orientation phonetics [G. Phonetik]: the study of the physical and physiological aspects of human sound production and
More informationA NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK. Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren
A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren Speech Technology and Research Laboratory, SRI International,
More informationInternational 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 informationSmall-Vocabulary Speech Recognition for Resource- Scarce Languages
Small-Vocabulary Speech Recognition for Resource- Scarce Languages Fang Qiao School of Computer Science Carnegie Mellon University fqiao@andrew.cmu.edu Jahanzeb Sherwani iteleport LLC j@iteleportmobile.com
More informationSpeech 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 informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
More informationLikelihood-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 informationA Neural Network GUI Tested on Text-To-Phoneme Mapping
A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis
More informationCLASSIFICATION OF PROGRAM Critical Elements Analysis 1. High Priority Items Phonemic Awareness Instruction
CLASSIFICATION OF PROGRAM Critical Elements Analysis 1 Program Name: Macmillan/McGraw Hill Reading 2003 Date of Publication: 2003 Publisher: Macmillan/McGraw Hill Reviewer Code: 1. X The program meets
More informationAutoregressive product of multi-frame predictions can improve the accuracy of hybrid models
Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models Navdeep Jaitly 1, Vincent Vanhoucke 2, Geoffrey Hinton 1,2 1 University of Toronto 2 Google Inc. ndjaitly@cs.toronto.edu,
More informationClass-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 informationIntra-talker Variation: Audience Design Factors Affecting Lexical Selections
Tyler Perrachione LING 451-0 Proseminar in Sound Structure Prof. A. Bradlow 17 March 2006 Intra-talker Variation: Audience Design Factors Affecting Lexical Selections Abstract Although the acoustic and
More informationUsing Articulatory Features and Inferred Phonological Segments in Zero Resource Speech Processing
Using Articulatory Features and Inferred Phonological Segments in Zero Resource Speech Processing Pallavi Baljekar, Sunayana Sitaram, Prasanna Kumar Muthukumar, and Alan W Black Carnegie Mellon University,
More informationSpeech Translation for Triage of Emergency Phonecalls in Minority Languages
Speech Translation for Triage of Emergency Phonecalls in Minority Languages Udhyakumar Nallasamy, Alan W Black, Tanja Schultz, Robert Frederking Language Technologies Institute Carnegie Mellon University
More informationBUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING
BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING Gábor Gosztolya 1, Tamás Grósz 1, László Tóth 1, David Imseng 2 1 MTA-SZTE Research Group on Artificial
More informationThe Perception of Nasalized Vowels in American English: An Investigation of On-line Use of Vowel Nasalization in Lexical Access
The Perception of Nasalized Vowels in American English: An Investigation of On-line Use of Vowel Nasalization in Lexical Access Joyce McDonough 1, Heike Lenhert-LeHouiller 1, Neil Bardhan 2 1 Linguistics
More informationSpeech 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 informationhave to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,
A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994
More informationCROSS-LANGUAGE MAPPING FOR SMALL-VOCABULARY ASR IN UNDER-RESOURCED LANGUAGES: INVESTIGATING THE IMPACT OF SOURCE LANGUAGE CHOICE
CROSS-LANGUAGE MAPPING FOR SMALL-VOCABULARY ASR IN UNDER-RESOURCED LANGUAGES: INVESTIGATING THE IMPACT OF SOURCE LANGUAGE CHOICE Anjana Vakil and Alexis Palmer University of Saarland Department of Computational
More informationAUTOMATIC 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 informationInvestigation on Mandarin Broadcast News Speech Recognition
Investigation on Mandarin Broadcast News Speech Recognition Mei-Yuh Hwang 1, Xin Lei 1, Wen Wang 2, Takahiro Shinozaki 1 1 Univ. of Washington, Dept. of Electrical Engineering, Seattle, WA 98195 USA 2
More informationFlorida Reading Endorsement Alignment Matrix Competency 1
Florida Reading Endorsement Alignment Matrix Competency 1 Reading Endorsement Guiding Principle: Teachers will understand and teach reading as an ongoing strategic process resulting in students comprehending
More informationEli 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 informationQuarterly 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 informationEnglish Language and Applied Linguistics. Module Descriptions 2017/18
English Language and Applied Linguistics Module Descriptions 2017/18 Level I (i.e. 2 nd Yr.) Modules Please be aware that all modules are subject to availability. If you have any questions about the modules,
More informationLetter-based speech synthesis
Letter-based speech synthesis Oliver Watts, Junichi Yamagishi, Simon King Centre for Speech Technology Research, University of Edinburgh, UK O.S.Watts@sms.ed.ac.uk jyamagis@inf.ed.ac.uk Simon.King@ed.ac.uk
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationVowel mispronunciation detection using DNN acoustic models with cross-lingual training
INTERSPEECH 2015 Vowel mispronunciation detection using DNN acoustic models with cross-lingual training Shrikant Joshi, Nachiket Deo, Preeti Rao Department of Electrical Engineering, Indian Institute of
More informationDOMAIN 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 informationBi-Annual Status Report For. Improved Monosyllabic Word Modeling on SWITCHBOARD
INSTITUTE FOR SIGNAL AND INFORMATION PROCESSING Bi-Annual Status Report For Improved Monosyllabic Word Modeling on SWITCHBOARD submitted by: J. Hamaker, N. Deshmukh, A. Ganapathiraju, and J. Picone Institute
More informationEdinburgh Research Explorer
Edinburgh Research Explorer Personalising speech-to-speech translation Citation for published version: Dines, J, Liang, H, Saheer, L, Gibson, M, Byrne, W, Oura, K, Tokuda, K, Yamagishi, J, King, S, Wester,
More informationPhonetic- 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 informationOn the Formation of Phoneme Categories in DNN Acoustic Models
On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-
More informationUTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation
UTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation Taufiq Hasan Gang Liu Seyed Omid Sadjadi Navid Shokouhi The CRSS SRE Team John H.L. Hansen Keith W. Godin Abhinav Misra Ali Ziaei Hynek Bořil
More informationQuarterly Progress and Status Report. Voiced-voiceless distinction in alaryngeal speech - acoustic and articula
Dept. for Speech, Music and Hearing Quarterly Progress and Status Report Voiced-voiceless distinction in alaryngeal speech - acoustic and articula Nord, L. and Hammarberg, B. and Lundström, E. journal:
More informationDNN ACOUSTIC MODELING WITH MODULAR MULTI-LINGUAL FEATURE EXTRACTION NETWORKS
DNN ACOUSTIC MODELING WITH MODULAR MULTI-LINGUAL FEATURE EXTRACTION NETWORKS Jonas Gehring 1 Quoc Bao Nguyen 1 Florian Metze 2 Alex Waibel 1,2 1 Interactive Systems Lab, Karlsruhe Institute of Technology;
More informationLaporan Penelitian Unggulan Prodi
Nama Rumpun Ilmu : Ilmu Sosial Laporan Penelitian Unggulan Prodi THE ROLE OF BAHASA INDONESIA IN FOREIGN LANGUAGE TEACHING AT THE LANGUAGE TRAINING CENTER UMY Oleh: Dedi Suryadi, M.Ed. Ph.D NIDN : 0504047102
More informationAustralia s tertiary education sector
Australia s tertiary education sector TOM KARMEL NHI NGUYEN NATIONAL CENTRE FOR VOCATIONAL EDUCATION RESEARCH Paper presented to the Centre for the Economics of Education and Training 7 th National Conference
More informationSARDNET: 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 informationRobust 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 informationUnsupervised Acoustic Model Training for Simultaneous Lecture Translation in Incremental and Batch Mode
Unsupervised Acoustic Model Training for Simultaneous Lecture Translation in Incremental and Batch Mode Diploma Thesis of Michael Heck At the Department of Informatics Karlsruhe Institute of Technology
More informationAutomatic English-Chinese name transliteration for development of multilingual resources
Automatic English-Chinese name transliteration for development of multilingual resources Stephen Wan and Cornelia Maria Verspoor Microsoft Research Institute Macquarie University Sydney NSW 2109, Australia
More informationDisambiguation of Thai Personal Name from Online News Articles
Disambiguation of Thai Personal Name from Online News Articles Phaisarn Sutheebanjard Graduate School of Information Technology Siam University Bangkok, Thailand mr.phaisarn@gmail.com Abstract Since online
More informationLEARNING A SEMANTIC PARSER FROM SPOKEN UTTERANCES. Judith Gaspers and Philipp Cimiano
LEARNING A SEMANTIC PARSER FROM SPOKEN UTTERANCES Judith Gaspers and Philipp Cimiano Semantic Computing Group, CITEC, Bielefeld University {jgaspers cimiano}@cit-ec.uni-bielefeld.de ABSTRACT Semantic parsers
More informationSegmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition
Segmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition Yanzhang He, Eric Fosler-Lussier Department of Computer Science and Engineering The hio
More informationThe IRISA Text-To-Speech System for the Blizzard Challenge 2017
The IRISA Text-To-Speech System for the Blizzard Challenge 2017 Pierre Alain, Nelly Barbot, Jonathan Chevelu, Gwénolé Lecorvé, Damien Lolive, Claude Simon, Marie Tahon IRISA, University of Rennes 1 (ENSSAT),
More informationExperiments with Cross-lingual Systems for Synthesis of Code-Mixed Text
Experiments with Cross-lingual Systems for Synthesis of Code-Mixed Text Sunayana Sitaram 1, Sai Krishna Rallabandi 1, Shruti Rijhwani 1 Alan W Black 2 1 Microsoft Research India 2 Carnegie Mellon University
More informationLower and Upper Secondary
Lower and Upper Secondary Type of Course Age Group Content Duration Target General English Lower secondary Grammar work, reading and comprehension skills, speech and drama. Using Multi-Media CD - Rom 7
More informationWHEN 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 informationImproved Hindi Broadcast ASR by Adapting the Language Model and Pronunciation Model Using A Priori Syntactic and Morphophonemic Knowledge
Improved Hindi Broadcast ASR by Adapting the Language Model and Pronunciation Model Using A Priori Syntactic and Morphophonemic Knowledge Preethi Jyothi 1, Mark Hasegawa-Johnson 1,2 1 Beckman Institute,
More informationPHONETIC DISTANCE BASED ACCENT CLASSIFIER TO IDENTIFY PRONUNCIATION VARIANTS AND OOV WORDS
PHONETIC DISTANCE BASED ACCENT CLASSIFIER TO IDENTIFY PRONUNCIATION VARIANTS AND OOV WORDS Akella Amarendra Babu 1 *, Ramadevi Yellasiri 2 and Akepogu Ananda Rao 3 1 JNIAS, JNT University Anantapur, Ananthapuramu,
More informationRole of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation
Role of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation Vivek Kumar Rangarajan Sridhar, John Chen, Srinivas Bangalore, Alistair Conkie AT&T abs - Research 180 Park Avenue, Florham Park,
More informationDOES RETELLING TECHNIQUE IMPROVE SPEAKING FLUENCY?
DOES RETELLING TECHNIQUE IMPROVE SPEAKING FLUENCY? Noor Rachmawaty (itaw75123@yahoo.com) Istanti Hermagustiana (dulcemaria_81@yahoo.com) Universitas Mulawarman, Indonesia Abstract: This paper is based
More informationStages of Literacy Ros Lugg
Beginning readers in the USA Stages of Literacy Ros Lugg Looked at predictors of reading success or failure Pre-readers readers aged 3-53 5 yrs Looked at variety of abilities IQ Speech and language abilities
More informationOn Developing Acoustic Models Using HTK. M.A. Spaans BSc.
On Developing Acoustic Models Using HTK M.A. Spaans BSc. On Developing Acoustic Models Using HTK M.A. Spaans BSc. Delft, December 2004 Copyright c 2004 M.A. Spaans BSc. December, 2004. Faculty of Electrical
More informationJournal of Phonetics
Journal of Phonetics 40 (2012) 595 607 Contents lists available at SciVerse ScienceDirect Journal of Phonetics journal homepage: www.elsevier.com/locate/phonetics How linguistic and probabilistic properties
More informationAnalysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription
Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription Wilny Wilson.P M.Tech Computer Science Student Thejus Engineering College Thrissur, India. Sindhu.S Computer
More informationOCR 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 informationELA/ELD Standards Correlation Matrix for ELD Materials Grade 1 Reading
ELA/ELD Correlation Matrix for ELD Materials Grade 1 Reading The English Language Arts (ELA) required for the one hour of English-Language Development (ELD) Materials are listed in Appendix 9-A, Matrix
More informationMANAGERIAL LEADERSHIP
MANAGERIAL LEADERSHIP MGMT 3287-002 FRI-132 (TR 11:00 AM-12:15 PM) Spring 2016 Instructor: Dr. Gary F. Kohut Office: FRI-308/CCB-703 Email: gfkohut@uncc.edu Telephone: 704.687.7651 (office) Office hours:
More information5/26/12. Adult L3 learners who are re- learning their L1: heritage speakers A growing trend in American colleges
International Seminar on Third Language Acquisition Vitoria- Gasteiz, May 24-25, 2012 Adult L3 learners who are re- learning their L1: heritage speakers A growing trend in American colleges Maria Polinsky
More informationFirst Grade Curriculum Highlights: In alignment with the Common Core Standards
First Grade Curriculum Highlights: In alignment with the Common Core Standards ENGLISH LANGUAGE ARTS Foundational Skills Print Concepts Demonstrate understanding of the organization and basic features
More informationUsing dialogue context to improve parsing performance in dialogue systems
Using dialogue context to improve parsing performance in dialogue systems Ivan Meza-Ruiz and Oliver Lemon School of Informatics, Edinburgh University 2 Buccleuch Place, Edinburgh I.V.Meza-Ruiz@sms.ed.ac.uk,
More informationLinguistics 220 Phonology: distributions and the concept of the phoneme. John Alderete, Simon Fraser University
Linguistics 220 Phonology: distributions and the concept of the phoneme John Alderete, Simon Fraser University Foundations in phonology Outline 1. Intuitions about phonological structure 2. Contrastive
More informationProceedings 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 informationLip 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 informationPobrane z czasopisma New Horizons in English Studies Data: 18/11/ :52:20. New Horizons in English Studies 1/2016
LANGUAGE Maria Curie-Skłodowska University () in Lublin k.laidler.umcs@gmail.com Online Adaptation of Word-initial Ukrainian CC Consonant Clusters by Native Speakers of English Abstract. The phenomenon
More informationListening and Speaking Skills of English Language of Adolescents of Government and Private Schools
Listening and Speaking Skills of English Language of Adolescents of Government and Private Schools Dr. Amardeep Kaur Professor, Babe Ke College of Education, Mudki, Ferozepur, Punjab Abstract The present
More informationArabic Orthography vs. Arabic OCR
Arabic Orthography vs. Arabic OCR Rich Heritage Challenging A Much Needed Technology Mohamed Attia Having consistently been spoken since more than 2000 years and on, Arabic is doubtlessly the oldest among
More informationAcoustic 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 informationBooks Effective Literacy Y5-8 Learning Through Talk Y4-8 Switch onto Spelling Spelling Under Scrutiny
By the End of Year 8 All Essential words lists 1-7 290 words Commonly Misspelt Words-55 working out more complex, irregular, and/or ambiguous words by using strategies such as inferring the unknown from
More informationRhythm-typology revisited.
DFG Project BA 737/1: "Cross-language and individual differences in the production and perception of syllabic prominence. Rhythm-typology revisited." Rhythm-typology revisited. B. Andreeva & W. Barry Jacques
More informationDIBELS Next BENCHMARK ASSESSMENTS
DIBELS Next BENCHMARK ASSESSMENTS Click to edit Master title style Benchmark Screening Benchmark testing is the systematic process of screening all students on essential skills predictive of later reading
More informationNon intrusive multi-biometrics on a mobile device: a comparison of fusion techniques
Non intrusive multi-biometrics on a mobile device: a comparison of fusion techniques Lorene Allano 1*1, Andrew C. Morris 2, Harin Sellahewa 3, Sonia Garcia-Salicetti 1, Jacques Koreman 2, Sabah Jassim
More informationSegregation 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 informationPhonological Processing for Urdu Text to Speech System
Phonological Processing for Urdu Text to Speech System Sarmad Hussain Center for Research in Urdu Language Processing, National University of Computer and Emerging Sciences, B Block, Faisal Town, Lahore,
More informationThe Effect of Extensive Reading on Developing the Grammatical. Accuracy of the EFL Freshmen at Al Al-Bayt University
The Effect of Extensive Reading on Developing the Grammatical Accuracy of the EFL Freshmen at Al Al-Bayt University Kifah Rakan Alqadi Al Al-Bayt University Faculty of Arts Department of English Language
More informationCross Language Information Retrieval
Cross Language Information Retrieval RAFFAELLA BERNARDI UNIVERSITÀ DEGLI STUDI DI TRENTO P.ZZA VENEZIA, ROOM: 2.05, E-MAIL: BERNARDI@DISI.UNITN.IT Contents 1 Acknowledgment.............................................
More informationPhonetics. The Sound of Language
Phonetics. The Sound of Language 1 The Description of Sounds Fromkin & Rodman: An Introduction to Language. Fort Worth etc., Harcourt Brace Jovanovich Read: Chapter 5, (p. 176ff.) (or the corresponding
More informationelearning OVERVIEW GFA Consulting Group GmbH 1
elearning OVERVIEW 23.05.2017 GFA Consulting Group GmbH 1 Definition E-Learning E-Learning means teaching and learning utilized by electronic technology and tools. 23.05.2017 Definition E-Learning GFA
More informationAn Online Handwriting Recognition System For Turkish
An Online Handwriting Recognition System For Turkish Esra Vural, Hakan Erdogan, Kemal Oflazer, Berrin Yanikoglu Sabanci University, Tuzla, Istanbul, Turkey 34956 ABSTRACT Despite recent developments in
More informationTextbook Evalyation:
STUDIES IN LITERATURE AND LANGUAGE Vol. 1, No. 8, 2010, pp. 54-60 www.cscanada.net ISSN 1923-1555 [Print] ISSN 1923-1563 [Online] www.cscanada.org Textbook Evalyation: EFL Teachers Perspectives on New
More informationReview in ICAME Journal, Volume 38, 2014, DOI: /icame
Review in ICAME Journal, Volume 38, 2014, DOI: 10.2478/icame-2014-0012 Gaëtanelle Gilquin and Sylvie De Cock (eds.). Errors and disfluencies in spoken corpora. Amsterdam: John Benjamins. 2013. 172 pp.
More informationTechnical Report #1. Summary of Decision Rules for Intensive, Strategic, and Benchmark Instructional
Beginning Kindergarten Decision Rules Page 1 IDEL : Indicadores Dinámicos del Éxito in la Lectura Technical Report #1 Summary of Decision Rules for Intensive, Strategic, and Benchmark Instructional Recommendations
More informationAtypical 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