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

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

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53-56).Tone plays a fundamental role in Mandarin Chinese, as it plays a lexical role in determining the meanings of words in spoken Mandarin. For example, these two sentences ... (I like horses) and ... (I like to scold) differ only in the tone carried by the last syllable. Thus, the inclusion of tone-related information through analysis of pitch data should improve the performance of automatic speech recognition (ASR) systems on Mandarin Chinese. The focus of this thesis is to improve the performance of a non-tonal automatic speech recognition (ASR) system on a Mandarin Chinese corpus by implementing modifications to the system code to incorporate pitch features. We compile and format a Mandarin Chinese broadcast new corpus for use with the ASR system, and implement a pitch feature extraction algorithm. Additionally, we investigate two algorithms for incorporating pitch features in Mandarin Chinese speech recognition. Firstly, we build and test a baseline tonal ASR system with embedded tone modeling by concatenating the cepstral and pitch feature vectors for use as the input to our phonetic model (a Hidden Markov Model, or HMM). We find that our embedded tone modeling algorithm does improve performance on Mandarin Chinese, showing that including tonal information is in fact contributive for Mandarin Chinese speech recognition. Secondly, we implement and test the effectiveness of HMM-based multistream models.by Karen Lingyun Chu.M.Eng