Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm

Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task

Robust speech recognition based on a Bayesian prediction approach

We study a category of robust speech recognition problem in which mismatches exist between training and testing conditions, and no accurate knowledge of the mismatch mechanism is available. The only available information is the test data along with a set of pretrained Gaussian mixture continuous density hidden Markov models (CDHMMs). We investigate the problem from the viewpoint of Bayesian prediction. A simple prior distribution, namely constrained uniform distribution, is adopted to characterize the uncertainty of the mean vectors of the CDHMMs. Two methods, namely a model compensation technique based on Bayesian predictive density and a robust decision strategy called Viterbi Bayesian predictive classification are studied. The proposed methods are compared with the conventional Viterbi decoding algorithm in speaker-independent recognition experiments on isolated digits and TI connected digit strings (TIDTGITS), where the mismatches between training and testing conditions are caused by: (1) additive Gaussian white noise, (2) each of 25 types of actual additive ambient noises, and (3) gender difference. The experimental results show that the adopted prior distribution and the proposed techniques help to improve the performance robustness under the examined mismatch conditions.published_or_final_versio

Linguistic constraints for large vocabulary speech recognition.

by Roger H.Y. Leung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 79-84).Abstracts in English and Chinese.ABSTRACT --- p.IKeywords: --- p.IACKNOWLEDGEMENTS --- p.IIITABLE OF CONTENTS: --- p.IVTable of Figures: --- p.VITable of Tables: --- p.VIIChapter CHAPTER 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Languages in the World --- p.2Chapter 1.2 --- Problems of Chinese Speech Recognition --- p.3Chapter 1.2.1 --- Unlimited word size: --- p.3Chapter 1.2.2 --- Too many Homophones: --- p.3Chapter 1.2.3 --- Difference between spoken and written Chinese: --- p.3Chapter 1.2.4 --- Word Segmentation Problem: --- p.4Chapter 1.3 --- Different types of knowledge --- p.5Chapter 1.4 --- Chapter Conclusion --- p.6Chapter CHAPTER 2 --- FOUNDATIONS --- p.7Chapter 2.1 --- Chinese Phonology and Language Properties --- p.7Chapter 2.1.1 --- Basic Syllable Structure --- p.7Chapter 2.2 --- Acoustic Models --- p.9Chapter 2.2.1 --- Acoustic Unit --- p.9Chapter 2.2.2 --- Hidden Markov Model (HMM) --- p.9Chapter 2.3 --- Search Algorithm --- p.11Chapter 2.4 --- Statistical Language Models --- p.12Chapter 2.4.1 --- Context-Independent Language Model --- p.12Chapter 2.4.2 --- Word-Pair Language Model --- p.13Chapter 2.4.3 --- N-gram Language Model --- p.13Chapter 2.4.4 --- Backoff n-gram --- p.14Chapter 2.5 --- Smoothing for Language Model --- p.16Chapter CHAPTER 3 --- LEXICAL ACCESS --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- Motivation： Phonological and lexical constraints --- p.20Chapter 3.3 --- Broad Classes Representation --- p.22Chapter 3.4 --- Broad Classes Statistic Measures --- p.25Chapter 3.5 --- Broad Classes Frequency Normalization --- p.26Chapter 3.6 --- Broad Classes Analysis --- p.27Chapter 3.7 --- Isolated Word Speech Recognizer using Broad Classes --- p.33Chapter 3.8 --- Chapter Conclusion --- p.34Chapter CHAPTER 4 --- CHARACTER AND WORD LANGUAGE MODEL --- p.35Chapter 4.1 --- Introduction --- p.35Chapter 4.2 --- Motivation --- p.36Chapter 4.2.1 --- Perplexity --- p.36Chapter 4.3 --- Call Home Mandarin corpus --- p.38Chapter 4.3.1 --- Acoustic Data --- p.38Chapter 4.3.2 --- Transcription Texts --- p.39Chapter 4.4 --- Methodology: Building Language Model --- p.41Chapter 4.5 --- Character Level Language Model --- p.45Chapter 4.6 --- Word Level Language Model --- p.48Chapter 4.7 --- Comparison of Character level and Word level Language Model --- p.50Chapter 4.8 --- Interpolated Language Model --- p.54Chapter 4.8.1 --- Methodology --- p.54Chapter 4.8.2 --- Experiment Results --- p.55Chapter 4.9 --- Chapter Conclusion --- p.56Chapter CHAPTER 5 --- N-GRAM SMOOTHING --- p.57Chapter 5.1 --- Introduction --- p.57Chapter 5.2 --- Motivation --- p.58Chapter 5.3 --- Mathematical Representation --- p.59Chapter 5.4 --- Methodology: Smoothing techniques --- p.61Chapter 5.4.1 --- Add-one Smoothing --- p.62Chapter 5.4.2 --- Witten-Bell Discounting --- p.64Chapter 5.4.3 --- Good Turing Discounting --- p.66Chapter 5.4.4 --- Absolute and Linear Discounting --- p.68Chapter 5.5 --- Comparison of Different Discount Methods --- p.70Chapter 5.6 --- Continuous Word Speech Recognizer --- p.71Chapter 5.6.1 --- Experiment Setup --- p.71Chapter 5.6.2 --- Experiment Results: --- p.72Chapter 5.7 --- Chapter Conclusion --- p.74Chapter CHAPTER 6 --- SUMMARY AND CONCLUSIONS --- p.75Chapter 6.1 --- Summary --- p.75Chapter 6.2 --- Further Work --- p.77Chapter 6.3 --- Conclusion --- p.78REFERENCE --- p.7