Study on training of a statistical acoustic model for speech recognition

制度:新 ; 文部省報告番号:乙1419号 ; 学位の種類:博士(情報科学) ; 授与年月日:1999/2/4 ; 早大学位記番号:新2744 ; 理工学図書館請求番号:2310本文PDFは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルをPDFに変換したものである

Whole Word Phonetic Displays for Speech Articulation Training

The main objective of this dissertation is to investigate and develop speech recognition technologies for speech training for people with hearing impairments. During the course of this work, a computer aided speech training system for articulation speech training was also designed and implemented. The speech training system places emphasis on displays to improve children\u27s pronunciation of isolated Consonant-Vowel-Consonant (CVC) words, with displays at both the phonetic level and whole word level. This dissertation presents two hybrid methods for combining Hidden Markov Models (HMMs) and Neural Networks (NNs) for speech recognition. The first method uses NN outputs as posterior probability estimators for HMMs. The second method uses NNs to transform the original speech features to normalized features with reduced correlation. Based on experimental testing, both of the hybrid methods give higher accuracy than standard HMM methods. The second method, using the NN to create normalized features, outperforms the first method in terms of accuracy. Several graphical displays were developed to provide real time visual feedback to users, to help them to improve and correct their pronunciations

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems

Continuous Density Hidden Markov Model for Hindi Speech Recognition

State of the art automatic speech recognitionsystem uses Mel frequency cepstral coefficients as featureextractor along with Gaussian mixture model for acousticmodeling but there is no standard value to assign number ofmixture component in speech recognition process.Currentchoice of mixture component is arbitrary with littlejustification. Also the standard set for European languagescan not be used in Hindi speech recognition due to mismatchin database size of the languages.Parameter estimation withtoo many or few component may inappropriately estimatethe mixture model. Therefore, number of mixture isimportant for initial estimation of expectation maximizationprocess. In this research work, the authors estimate numberof Gaussian mixture component for Hindi database basedupon the size of vocabulary.Mel frequency cepstral featureand perceptual linear predictive feature along with itsextended variations with delta-delta-delta feature have beenused to evaluate this number based on optimal recognitionscore of the system . Comparitive analysis of recognitionperformance for both the feature extraction methods onmedium size Hindi database is also presented in thispaper.HLDA has been used as feature reduction techniqueand also its impact on the recognition score has beenhighlighted

Continuous speech phoneme recognition using neural networks and grammar correction.

by Wai-Tat Fu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 104-[109]).Chapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Problem of Speech Recognition --- p.1Chapter 1.2 --- Why continuous speech recognition? --- p.5Chapter 1.3 --- Current status of continuous speech recognition --- p.6Chapter 1.4 --- Research Goal --- p.10Chapter 1.5 --- Thesis outline --- p.10Chapter 2 --- Current Approaches to Continuous Speech Recognition --- p.12Chapter 2.1 --- BASIC STEPS FOR CONTINUOUS SPEECH RECOGNITION --- p.12Chapter 2.2 --- THE HIDDEN MARKOV MODEL APPROACH --- p.16Chapter 2.2.1 --- Introduction --- p.16Chapter 2.2.2 --- Segmentation and Pattern Matching --- p.18Chapter 2.2.3 --- Word Formation and Syntactic Processing --- p.22Chapter 2.2.4 --- Discussion --- p.23Chapter 2.3 --- NEURAL NETWORK APPROACH --- p.24Chapter 2.3.1 --- Introduction --- p.24Chapter 2.3.2 --- Segmentation and Pattern Matching --- p.25Chapter 2.3.3 --- Discussion --- p.27Chapter 2.4 --- MLP/HMM HYBRID APPROACH --- p.28Chapter 2.4.1 --- Introduction --- p.28Chapter 2.4.2 --- Architecture of Hybrid MLP/HMM Systems --- p.29Chapter 2.4.3 --- Discussions --- p.30Chapter 2.5 --- SYNTACTIC GRAMMAR --- p.30Chapter 2.5.1 --- Introduction --- p.30Chapter 2.5.2 --- Word formation and Syntactic Processing --- p.31Chapter 2.5.3 --- Discussion --- p.32Chapter 2.6 --- SUMMARY --- p.32Chapter 3 --- Neural Network As Pattern Classifier --- p.34Chapter 3.1 --- INTRODUCTION --- p.34Chapter 3.2 --- TRAINING ALGORITHMS AND TOPOLOGIES --- p.35Chapter 3.2.1 --- Multilayer Perceptrons --- p.35Chapter 3.2.2 --- Recurrent Neural Networks --- p.39Chapter 3.2.3 --- Self-organizing Maps --- p.41Chapter 3.2.4 --- Learning Vector Quantization --- p.43Chapter 3.3 --- EXPERIMENTS --- p.44Chapter 3.3.1 --- The Data Set --- p.44Chapter 3.3.2 --- Preprocessing of the Speech Data --- p.45Chapter 3.3.3 --- The Pattern Classifiers --- p.50Chapter 3.4 --- RESULTS AND DISCUSSIONS --- p.53Chapter 4 --- High Level Context Information --- p.56Chapter 4.1 --- INTRODUCTION --- p.56Chapter 4.2 --- HIDDEN MARKOV MODEL APPROACH --- p.57Chapter 4.3 --- THE DYNAMIC PROGRAMMING APPROACH --- p.59Chapter 4.4 --- THE SYNTACTIC GRAMMAR APPROACH --- p.60Chapter 5 --- Finite State Grammar Network --- p.62Chapter 5.1 --- INTRODUCTION --- p.62Chapter 5.2 --- THE GRAMMAR COMPILATION --- p.63Chapter 5.2.1 --- Introduction --- p.63Chapter 5.2.2 --- K-Tails Clustering Method --- p.66Chapter 5.2.3 --- Inference of finite state grammar --- p.67Chapter 5.2.4 --- Error Correcting Parsing --- p.69Chapter 5.3 --- EXPERIMENT --- p.71Chapter 5.4 --- RESULTS AND DISCUSSIONS --- p.73Chapter 6 --- The Integrated System --- p.81Chapter 6.1 --- INTRODUCTION --- p.81Chapter 6.2 --- POSTPROCESSING OF NEURAL NETWORK OUTPUT --- p.82Chapter 6.2.1 --- Activation Threshold --- p.82Chapter 6.2.2 --- Duration Threshold --- p.85Chapter 6.2.3 --- Merging of Phoneme boundaries --- p.88Chapter 6.3 --- THE ERROR CORRECTING PARSER --- p.90Chapter 6.4 --- RESULTS AND DISCUSSIONS --- p.96Chapter 7 --- Conclusions --- p.101Bibliography --- p.10