After decades of research activity, speech recognition technologies have advanced in both the theoretical and practical domains. The technology of speech recognition has evolved from the first attempts at speech analysis with digital computers by James Flanagan’s group at Bell Laboratories in the early 1960s, through to the introduction of dynamic time-warping pattern-matching techniques in the 1970s, which laid the foundations for the statistical modeling of speech in the 1980s that was pursued by Fred Jelinek and Jim Baker from IBM’s T. J. Watson Research Center. In the years 1980-90, when Lawrence H. Rabiner introduced hidden Markov models to speech recognition, a statistical approach became ubiquitous in speech processing. This established the core technology of speech recognition and started the era of modern speech recognition engines. In the 1990s several efforts were made to increase the accuracy of speech recognition systems by modeling the speech with large amounts of speech data and by performing extensive evaluations of speech recognition in various tasks and in different languages. The degree of maturity reached by speech recognition technologies during these years also allowed the development of practical applications for voice human–computer interaction and audioinformation retrieval. The great potential of such applications moved the focus of the research from recognizing the speech, collected in controlled environments and limited to strictly domain-oriented content, towards the modeling of conversational speech, with all its variability and language-specific problems. This has yielded the next generation of speech recognition systems, which aim to reliably recognize large-scale vocabulary, continuous speech, even in adverse acoustic environments and under different operating conditions. As such, the main issues today have become the robustness and scalability of automatic speech recognition systems and their integration into other speech processing applications. This book on Speech Recognition Technologies and Applications aims to address some of these issues.
Throughout the book the authors describe unique research problems together with their solutions in various areas of speech processing, with the emphasis on the robustness of the presented approaches and on the integration of language-specific information into speech recognition and other speech processing applications. The 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.
We would like to thank all the authors who have contributed to this book. For our part, we hope that by reading this book you will get many helpful ideas for your own research, which will help to bridge the gap between speech-recognition technology and applications.