From 'Harmonic Telegraph' to Cellular Phones.- Challenges in Speech Coding Research.- Recent Speech Coding Technologies and Standards.- Ensemble Learning Approaches in Speech Recognition.- Dynamic and Deep Networks For Speech Modeling and Recognition.- Speech Based Emotion Recognition.- Speaker Diarization: Challenges and Emerging Research.- Maximum a posteriori spectral estimation with source log-spectral priors
for multichannel speech enhancement.- Modulation Processing for Speech Enhancement.
Über den Autor
Tokunbo Ogunfunmi is an Associate Professor of Electrical Engineering and an Associate Dean for Research and Fac. Dev. at Santa Clara University.
Roberto Togneri is a professor with the School of Electrical, Electronic and Computer Engineering at The University of Western Australia.
Madihally (Sim) Narasimha is a Senior Director of Technology at Qualcomm Inc.
This book describes the basic principles underlying the generation, coding, transmission and enhancement of speech and audio signals, including advanced statistical and machine learning techniques for speech and speaker recognition with an overview of the key innovations in these areas. Key research undertaken in speech coding, speech enhancement, speech recognition, emotion recognition and speaker diarization are also presented, along with recent advances and new paradigms in these areas.
Offers readers a single-source reference on the significant applications of speech and audio processing to speech coding, speech enhancement and speech/speaker recognition. Enables readers involved in algorithm development and implementation issues for speech coding to understand the historical development and future challenges in speech coding research
Discusses speech coding methods yielding bit-streams that are multi-rate and scalable for Voice-over-IP (VoIP) Networks
Presents an overview of recent developments in conversational speech coding technologies, important new algorithmic advances, and recent standardization activities in ITU-T, 3GPP, 3GPP2, MPEG and IETF that offer a significantly improved user experience during voice calls on existing and future communication systems
Presents an overview of ensemble learning efforts based on different machine learning techniques that have emerged in automatic speech recognition in recent years
Emphasizes signal processing for efficient time-domain and spectral-domain representations, reduction of noise, channel and session variabilities, extraction of temporal and spectral features for recognition and modeling
Informs readers of the latest research and developments in advanced statistical estimation and deep neural networks for speech recognition
Presents readers with the architectural framework and key approaches involved in the "hot" research areas of emotion recognition and speaker diairization systems
Provides readers with a more enriching view of state of the art research in speech enhancement arising from novel multi-microphone and time-frequency solutions