Toolkits for ASR; Sphinx

Similar documents
Learning Methods in Multilingual Speech Recognition

Modeling function word errors in DNN-HMM based LVCSR systems

Modeling function word errors in DNN-HMM based LVCSR systems

Speech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers

BAUM-WELCH TRAINING FOR SEGMENT-BASED SPEECH RECOGNITION. Han Shu, I. Lee Hetherington, and James Glass

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines

Speech Recognition at ICSI: Broadcast News and beyond

BUILDING CONTEXT-DEPENDENT DNN ACOUSTIC MODELS USING KULLBACK-LEIBLER DIVERGENCE-BASED STATE TYING

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION

A NOVEL SCHEME FOR SPEAKER RECOGNITION USING A PHONETICALLY-AWARE DEEP NEURAL NETWORK. Yun Lei Nicolas Scheffer Luciana Ferrer Mitchell McLaren

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

On the Formation of Phoneme Categories in DNN Acoustic Models

Investigation on Mandarin Broadcast News Speech Recognition

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

Using Articulatory Features and Inferred Phonological Segments in Zero Resource Speech Processing

Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models

Human Emotion Recognition From Speech

Unsupervised Acoustic Model Training for Simultaneous Lecture Translation in Incremental and Batch Mode

Speaker recognition using universal background model on YOHO database

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

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

Segmental Conditional Random Fields with Deep Neural Networks as Acoustic Models for First-Pass Word Recognition

A study of speaker adaptation for DNN-based speech synthesis

Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition

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

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

Unvoiced Landmark Detection for Segment-based Mandarin Continuous Speech Recognition

Speech Emotion Recognition Using Support Vector Machine

Lecture 9: Speech Recognition

Edinburgh Research Explorer

Speech Recognition by Indexing and Sequencing

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

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

Letter-based speech synthesis

Calibration of Confidence Measures in Speech Recognition

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

Vimala.C Project Fellow, Department of Computer Science Avinashilingam Institute for Home Science and Higher Education and Women Coimbatore, India

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

STUDIES WITH FABRICATED SWITCHBOARD DATA: EXPLORING SOURCES OF MODEL-DATA MISMATCH

Improved Hindi Broadcast ASR by Adapting the Language Model and Pronunciation Model Using A Priori Syntactic and Morphophonemic Knowledge

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

On Developing Acoustic Models Using HTK. M.A. Spaans BSc.

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

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

Automatic Pronunciation Checker

A Neural Network GUI Tested on Text-To-Phoneme Mapping

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

Analysis of Speech Recognition Models for Real Time Captioning and Post Lecture Transcription

Bi-Annual Status Report For. Improved Monosyllabic Word Modeling on SWITCHBOARD

Speech Translation for Triage of Emergency Phonecalls in Minority Languages

International Journal of Advanced Networking Applications (IJANA) ISSN No. :

Small-Vocabulary Speech Recognition for Resource- Scarce Languages

INVESTIGATION OF UNSUPERVISED ADAPTATION OF DNN ACOUSTIC MODELS WITH FILTER BANK INPUT

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

CROSS-LANGUAGE MAPPING FOR SMALL-VOCABULARY ASR IN UNDER-RESOURCED LANGUAGES: INVESTIGATING THE IMPACT OF SOURCE LANGUAGE CHOICE

An Online Handwriting Recognition System For Turkish

SEMI-SUPERVISED ENSEMBLE DNN ACOUSTIC MODEL TRAINING

Deep Neural Network Language Models

Masters Thesis CLASSIFICATION OF GESTURES USING POINTING DEVICE BASED ON HIDDEN MARKOV MODEL

ACOUSTIC EVENT DETECTION IN REAL LIFE RECORDINGS

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

Investigation of Indian English Speech Recognition using CMU Sphinx

LOW-RANK AND SPARSE SOFT TARGETS TO LEARN BETTER DNN ACOUSTIC MODELS

Vowel mispronunciation detection using DNN acoustic models with cross-lingual training

Distributed Learning of Multilingual DNN Feature Extractors using GPUs

Role of Pausing in Text-to-Speech Synthesis for Simultaneous Interpretation

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Lower and Upper Secondary

Characterizing and Processing Robot-Directed Speech

Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling

Improvements to the Pruning Behavior of DNN Acoustic Models

Proceedings of Meetings on Acoustics

UTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation

PHONETIC DISTANCE BASED ACCENT CLASSIFIER TO IDENTIFY PRONUNCIATION VARIANTS AND OOV WORDS

Speaker Identification by Comparison of Smart Methods. Abstract

Generative models and adversarial training

WHEN THERE IS A mismatch between the acoustic

Support Vector Machines for Speaker and Language Recognition

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

NCU IISR English-Korean and English-Chinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches

WiggleWorks Software Manual PDF0049 (PDF) Houghton Mifflin Harcourt Publishing Company

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

Lecture 1: Machine Learning Basics

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

Books Effective Literacy Y5-8 Learning Through Talk Y4-8 Switch onto Spelling Spelling Under Scrutiny

have 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,

Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I

Large vocabulary off-line handwriting recognition: A survey

Effect of Word Complexity on L2 Vocabulary Learning

SIE: Speech Enabled Interface for E-Learning

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

Implementing a tool to Support KAOS-Beta Process Model Using EPF

School of Innovative Technologies and Engineering

English Language and Applied Linguistics. Module Descriptions 2017/18

ASR for Tajweed Rules: Integrated with Self- Learning Environments

Non intrusive multi-biometrics on a mobile device: a comparison of fusion techniques

Natural Language Processing. George Konidaris

Segregation of Unvoiced Speech from Nonspeech Interference

Switchboard Language Model Improvement with Conversational Data from Gigaword

Transcription:

Toolkits for ASR; Sphinx Samudravijaya K samudravijaya@gmail.com 08-MAR-2011 Workshop on Fundamentals of Automatic Speech Recognition CDAC Noida, 08-MAR-2011 Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 1/31

A Block Diagram of an ASR System Signal Feature Extraction Training Acoustic Model Testing Matching (acoustic domain) Symbol sequence Language Model Matching (symbolic domain) Sentence Hypothesis Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 2/31

Hierachy of Units in an Utterance source: state of art ASR by Steve Young, 2000 Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 3/31

Sentence HMM is composed of Phone HMMs Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 4/31

Toolkits for Automatic Speech Recognition (1) Training, (2) Testing, (3) Performance Evaluation There are several public domain toolkits that help to build an ASR system: HTK: Hidden Markov Model ToolKit [1]. Public domain, but decoder cannot be distributed (C). Sphinxes [2]: Open source: (C, C++, java) Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 5/31

Toolkits for Automatic Speech Recognition (1) Training, (2) Testing, (3) Performance Evaluation There are several public domain toolkits that help to build an ASR system: HTK: Hidden Markov Model ToolKit [1]. Public domain, but decoder cannot be distributed (C). Sphinxes [2]: Open source: (C, C++, java) ISIP Production system [3]. Public domain ( without any restrictions) (C++) Julius Open-Source Large Vocabulary CSR Engine [4]. It uses Acoustic Models in HTK format, and Grammar files in its own format. Open license (no limitations on distribution) (C++). HMM toolbox for Matlab Useful for Isolated Word Recognition [5]. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 5/31

What is CMU Sphinx? According to Arthur Chan (the editor of Hieroglyphs[6], the sphinx manual in a book form), there are two definitions of Sphinx: A large vocabulary speech recognizer with high accuracy and speed performance. A collection of tools and resources that enables developers/researchers to build successful speech recognizers Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 6/31

Pocketsphinx source: SphinxLunch20041021.ppt by Arthur Chan, 2004 Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 7/31

A Block Diagram of an ASR System Signal Feature Extraction Training Acoustic Model Testing Matching (acoustic domain) Symbol sequence Language Model Matching (symbolic domain) Sentence Hypothesis Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 8/31

Language model training source: state of art ASR by Steve Young, 2000 Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 9/31

CMU-Cambridge SLM toolkit Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 10/31

Lexicon (Pronunciation Dictionary) source: Ph.D. thesis of Ravi Shankar M., CMU [7] Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 11/31

source: http://speech.tifr.res.in/resources/data/labelsetasr100815.pdf Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 12/31

source: www.liacs.nl/ erwin/sr2003/sphinx.ppt Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 13/31

Feature Extraction (Frontend processing) * wave2feat program computes 13 MFCCs from speech files stored in any of wav,nist,raw format. * Caution: use -dither yes option. Excise long silences. * cepview s0001.cep prints the cepstral coefficients. source: Ph.D. thesis of Ravi Shankar M., CMU [7]. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 14/31

SphinxTrain Training sub-word HMMs Stages of training (Reference: http://www.speech.cs.cmu.edu/sphinxman/fr4.html): 1 Training context Independent phone HMMs 2 Training context Dependent phone HMMs 3 Decision tree building 4 Training context Dependent tied phone HMMs 5 Recursive Gaussian splitting Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 15/31

Training Context Independent phone HMMs 2 steps: Initialization and Embedding re-estimation. Inputs: * Feature vector sequences * Word-level transcriptions * Pronunciation dictionary Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 16/31

Training Context Independent phone HMMs 2 steps: Initialization and Embedding re-estimation. Inputs: * Feature vector sequences * Word-level transcriptions * Pronunciation dictionary (I) Initialization: 1 Make a proto-type HMM (5-state, left-to-right, skipping 1 state permitted); copy to all phone HMMs. 2 Compute means and variance of all training feature vectors 3 Initialise Gaussians of all states of phone HMMs with global means and variance. 4 For each and every utterance, generate phone-level transcriptions from word-level transcriptions using the pronunciation dictionary. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 16/31

Training subword HMMs An iterative algorithm (Baum-Welch, also known as Forward-Backward) is used. The Maximum Likelihood approach guarantees increase of the likelihood of the trained model matching with training data with each iteration. To begin with, an initial estimation of parameters of HMMs (A,B,π) is required. Q: How to get an initial estimation of (λ = {A,B,π}? A: We can estimate parameters if we know the boundaries of every subword HMM in training utterances. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 17/31

Training subword HMMs An iterative algorithm (Baum-Welch, also known as Forward-Backward) is used. The Maximum Likelihood approach guarantees increase of the likelihood of the trained model matching with training data with each iteration. To begin with, an initial estimation of parameters of HMMs (A,B,π) is required. Q: How to get an initial estimation of (λ = {A,B,π}? A: We can estimate parameters if we know the boundaries of every subword HMM in training utterances. Practical solution: Assume that the durations of all units (phones) are equal. If there are N phones in a training utterance, divide the feature vector sequence into N equal parts. Assign each part, to a phoneme in the phoneme sequence corresponding to the transcription of the utterance. Repeat for all training utterances. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 17/31

Initial estimation of HMM parameters: an illustration Let the transcription of the 1st wave file be the following sequence of words: mera bhaarat mahaan Let the relevant lines in the dictionary be as follows: bhaarata bh aa r a t mahaana m a h aa n mera m e r aa The phonemehmm sequence (of length 16) corresponding to this sentence is sil m e r aa bh aa r a t m a h aa n sil Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 18/31

Initial estimation of HMM parameters: an illustration Let the transcription of the 1st wave file be the following sequence of words: mera bhaarat mahaan Let the relevant lines in the dictionary be as follows: bhaarata bh aa r a t mahaana m a h aa n mera m e r aa The phonemehmm sequence (of length 16) corresponding to this sentence is sil m e r aa bh aa r a t m a h aa n sil If the duration of the wavefile is 1.0sec, there will 98 feature vectors (frame shift = 10msec and frame size = 25msec). Assign the first 6 feature vectors to sil HMM; the next 6 (7 through 12) to m ; the next 6 (13 through 18) to e ;... ; the last 8 feature vectors to sil. If HMM has 3 states, assign 2 feature vector to each state; compute mean,sd. Assume a i,j =0.5 if j=i or j=i+1; else assign 0. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 18/31

Embedded Re-estimation (II) Embedding re-estimation: 1 For each utterance, do the following: Using the phone-level transcriptions, compose a sentence HMM out of phone HMMs. Forward-Backward algorithm: compute the likelihood of each feature vector being generated by each state of each phone HMM in the sentence HMM Accumulate likelihoods of feature vectors being generated by each state. 2 For each state: re-estimate HMM parameters using the accumulated likelihoods. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 19/31

Embedded Re-estimation (II) Embedding re-estimation: 1 For each utterance, do the following: Using the phone-level transcriptions, compose a sentence HMM out of phone HMMs. Forward-Backward algorithm: compute the likelihood of each feature vector being generated by each state of each phone HMM in the sentence HMM Accumulate likelihoods of feature vectors being generated by each state. 2 For each state: re-estimate HMM parameters using the accumulated likelihoods. Repeat the Embedded Re-estimation a few times. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 19/31

Training Context Dependent phone HMMs 1 Initialise N 3 triphone models, where N is the number of phones. 2 Compose sentence HMM out of triphone (CD) models instead of monophone (CI) models. 3 Carry out the Embedded Re-estimation for a few iterations. The sequence of CI HMMs was sil m e r aa bh aa r a t m a h aa n sil The sequence of CD HMMs (triphones) is sil sil-m+e m-e+r e-r+aa r-aa+bh... Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 20/31

Training Context Dependent phone HMMs 1 Initialise N 3 triphone models, where N is the number of phones. 2 Compose sentence HMM out of triphone (CD) models instead of monophone (CI) models. 3 Carry out the Embedded Re-estimation for a few iterations. The sequence of CI HMMs was sil m e r aa bh aa r a t m a h aa n sil The sequence of CD HMMs (triphones) is sil sil-m+e m-e+r e-r+aa r-aa+bh... If N = 50, each HMM has 3 states, there may be upto 375,000 states. Each state is associated with one Gaussian. Huge amount of speech data is needed for robust estimation of the parameters (µ,σ) of 375,000 Gaussians! Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 20/31

Training Context Dependent phone HMMs 1 Initialise N 3 triphone models, where N is the number of phones. 2 Compose sentence HMM out of triphone (CD) models instead of monophone (CI) models. 3 Carry out the Embedded Re-estimation for a few iterations. The sequence of CI HMMs was sil m e r aa bh aa r a t m a h aa n sil The sequence of CD HMMs (triphones) is sil sil-m+e m-e+r e-r+aa r-aa+bh... If N = 50, each HMM has 3 states, there may be upto 375,000 states. Each state is associated with one Gaussian. Huge amount of speech data is needed for robust estimation of the parameters (µ,σ) of 375,000 Gaussians! Reduce the number of states by state-tying; use Decision Trees. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 20/31

Training Context Dependent tied phone HMMs * Build Decision Trees for parameter sharing. * One decision tree is built for each state position (5 decision trees if there are 5 emitting states of HMMs). Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 21/31

Training Context Dependent tied phone HMMs * Build Decision Trees for parameter sharing. * One decision tree is built for each state position (5 decision trees if there are 5 emitting states of HMMs). The first step is to generate Linguistic Questions. Two methods: 1 Manually create linguistic questions using phonetic knowledge. 2 Run make quests program to automatically form phone groups. First few lines of a linguistic-questions file may look like this. SIL sil h s sh VOWELS a aa i ii u uu e ee o oo NASAL m n ng LABPLO p ph b bh Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 21/31

Decision trees are used to decide which of the HMM states of all the triphones (seen and unseen) are similar to each other, so that data from all these states are collected together and used to train one global state, which is called a senone (also called a tied state). Example: Left states of 1st and 3rd triphones above would be similar. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 22/31

Training Context Dependent tied phone HMMs 1 Prune the Decision trees so that the number of senones (tied states) is commensurate with the amount of training data. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 23/31

Training Context Dependent tied phone HMMs 1 Prune the Decision trees so that the number of senones (tied states) is commensurate with the amount of training data. 2 Create CD tied model definition file that has (a) all triphones which are seen during training, and (b) has the states corresponding to these triphones identified with senones from the pruned trees (state-senone mapping). 3 Carry out the Embedded Re-estimation (tied CD models) for a few iterations. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 23/31

Training Context Dependent tied phone HMMs 1 Prune the Decision trees so that the number of senones (tied states) is commensurate with the amount of training data. 2 Create CD tied model definition file that has (a) all triphones which are seen during training, and (b) has the states corresponding to these triphones identified with senones from the pruned trees (state-senone mapping). 3 Carry out the Embedded Re-estimation (tied CD models) for a few iterations. 4 Generate Gaussian mixtures for each senone (tied state) and re-train. Repeat this step till the desired number (say 8) of mixtures are created for each GMM (senone). Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 23/31

Training Context Dependent tied phone HMMs 1 Prune the Decision trees so that the number of senones (tied states) is commensurate with the amount of training data. 2 Create CD tied model definition file that has (a) all triphones which are seen during training, and (b) has the states corresponding to these triphones identified with senones from the pruned trees (state-senone mapping). 3 Carry out the Embedded Re-estimation (tied CD models) for a few iterations. 4 Generate Gaussian mixtures for each senone (tied state) and re-train. Repeat this step till the desired number (say 8) of mixtures are created for each GMM (senone). 5 One can carry out discriminative training following the Maximum Mutual Information Estimation scheme (maximises the posterior probability of the correct word sequence given all possible word sequences) [9]. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 23/31

source: www.liacs.nl/ erwin/sr2003/sphinx.ppt Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 24/31

Inputs to sphinx3 decoder source: www.liacs.nl/ erwin/sr2003/sphinx.ppt Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 25/31

Sphinx3 decoders source: www.liacs.nl/ erwin/sr2003/sphinx.ppt Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 26/31

Output of recogniser source: www.liacs.nl/ erwin/sr2003/sphinx.ppt Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 27/31

Samudravijaya source: K SphinxLunch20041021.ppt samudravijaya@gmail.com bytoolkits Arthur for ASR; Chan, Sphinx2004 28/31

Sphinx4 Sphinx-4 is a state-of-the-art speech recognition system written entirely in the Java programming language [10]. Generalized pluggable front end architecture: MFCC, CMN Generalized pluggable language model architecture: trigram, JSGF and ARPA-format FST grammars. Generalized acoustic model architecture: Sphinx-3 acoustic models. Generalized search management: breadth first and word pruning Post-processing recognition results: obtaining confidence scores, generating lattices. Standalone tools: displaying waveforms and spectrograms; generating features from audio. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 29/31

Comparison of Performance of Sphinxes source: [10]. PocketSphinx[11]: It is a small-footprint continuous speech recognition system, suitable for handheld and desktop applications. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 30/31

Sphinx, the eternal mystery source: [10]. Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 31/31

Bibliography Cambridge University, UK; Entropic; Microsoft HTK, Hidden Markov Model ToolKit http://htk.eng.cam.ac.uk/ Project by Carnegie Mellon University The CMU Sphinx group open source speech recognition engines http://cmusphinx.sourceforge.net/html/cmusphinx.php Joe Picone et al. ISIP Production system (r02 n02) (23-JUL-2009) http://www.isip.piconepress.com/projects/speech/software/ Japanese Universities and Laboratories Open-Source Large Vocabulary CSR Engine: Julius http://julius.sourceforge.jp/en/ Kevin Murphy HMM toolbox for Matlab Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 31/31

http://www.cs.ubc.ca/ murphyk/software/hmm/hmm.html Arthur Chan Hieroglyphs: Building Speech Application Using Sphinx and Related Resources, (3rd Draft) 11-MAR-2007 http://www.cs.cmu.edu/ãrchan/sphinxdoc.html Ravishankar M., Efficient Algorithms for Speech Recognition Ph.D Thesis, Carnegie Mellon University, May 1996, Tech Report. CMU-CS-96-143 http://www.cs.cmu.edu/ rkm/th/th.pdf Cambridge University, UK; Entropic; Microsoft HTK Book, Documentation of HTK http://htk.eng.cam.ac.uk/docs/docs.shtml L Qin and A Rudnicky Implementing and Improving MMIE Training in SphinxTrain CMU Sphinx Workshop 2010, 13 March 2010, Dallas, USA http://www.cs.cmu.edu/ sphinx/sphinx2010/papers/107.unblinded.p Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 31/31

Bhiksharaj et al. A speech recognizer written entirely in the Java programming language http://cmusphinx.sourceforge.net/sphinx4/ A small-footprint continuous speech recognition system http://cmusphinx.sourceforge.net/2010/03/pocketsphinx-0-6- release/ Samudravijaya K samudravijaya@gmail.com Toolkits for ASR; Sphinx 31/31

2024 © educationdocbox.com Privacy Policy | Terms of Service | Feedback