https://acousticresearchspeakers.net Sat, 19 Sep 2026 22:32:58 +0000 en-CA hourly 1 https://wordpress.org/?v=4.1.1 https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j-2/ https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j-2/#comments Tue, 30 Apr 2024 07:57:15 +0000 https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j-2/ read more »]]>


Machine learning has been playing a crucial role in these applications where the model parameters could be learned and the system performance could be optimized. As for speaker recognition, researchers and engineers have been attempting to tackle the most di cult challenges: noise robustness and domain mismatch. These e orts have now been fruitful, leading to commercial products starting to emerge. Voice authentication for e-banking and speaker identication in smart speakers. Research in speaker recognition has traditionally been focused on signal processing (for extracting the most relevant and robust features) and machine learning (for classifying the features). Recently, we have witnessed the shift in the focus from signal processing to machine learning. In particular, many studies have shown that model adaptation can address both robustness and domain mismatch. As for robust feature extraction, recent studies also demonstrate that deep learning and feature learning can be a great alternative to traditional signal processing algorithms. This book has two perspectives: Machine Learning and Speaker Recognition. The machine learning perspective gives readers insights on what make stateof-the-art systems perform so well. The speaker recognition perspective enables readers to apply machine learning techniques to address practical issues. Robustness under adverse acoustic environments and domain mismatch when deploying speaker recognition systems. The theories and practices of speaker recognition are tightly connected in th.

]]> https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j-2/feed/ 0 https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j/ https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j/#comments Fri, 26 Apr 2024 19:39:07 +0000 https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j/ read more »]]>


Machine learning has been playing a crucial role in these applications where the model parameters could be learned and the system performance could be optimized. As for speaker recognition, researchers and engineers have been attempting to tackle the most di cult challenges: noise robustness and domain mismatch. These e orts have now been fruitful, leading to commercial products starting to emerge. Voice authentication for e-banking and speaker identication in smart speakers. Research in speaker recognition has traditionally been focused on signal processing (for extracting the most relevant and robust features) and machine learning (for classifying the features). Recently, we have witnessed the shift in the focus from signal processing to machine learning. In particular, many studies have shown that model adaptation can address both robustness and domain mismatch. As for robust feature extraction, recent studies also demonstrate that deep learning and feature learning can be a great alternative to traditional signal processing algorithms. This book has two perspectives: Machine Learning and Speaker Recognition. The machine learning perspective gives readers insights on what make stateof-the-art systems perform so well. The speaker recognition perspective enables readers to apply machine learning techniques to address practical issues. Robustness under adverse acoustic environments and domain mismatch when deploying speaker recognition systems. The theories and practices of speaker recognition are tightly connected in th.

]]> https://acousticresearchspeakers.net/2024/04/machine-learning-for-speaker-recognition-hardcover-by-mak-man-wai-chien-j/feed/ 0 https://acousticresearchspeakers.net/2024/04/automatic-speech-and-speaker-recognition-advanced-topics-by-chin-hui-lee-engli/ https://acousticresearchspeakers.net/2024/04/automatic-speech-and-speaker-recognition-advanced-topics-by-chin-hui-lee-engli/#comments Thu, 25 Apr 2024 19:36:52 +0000 https://acousticresearchspeakers.net/2024/04/automatic-speech-and-speaker-recognition-advanced-topics-by-chin-hui-lee-engli/ read more »]]>



Automatic Speech and Speaker Recognition. By Chin-Hui Lee, Frank K. Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modelling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming-based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. This work groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modelling of fundamental speech units and lexical modelling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition-system realization. Chin-Hui Lee, Frank K. Grand Eagle Retail is the ideal place for all your shopping needs! We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. Please contact Customer Services and request “Return Authorisation” before you send your item back to us. We cannot take responsibility for items which are lost or damaged in transit. Home, Garden & Pets.

]]> https://acousticresearchspeakers.net/2024/04/automatic-speech-and-speaker-recognition-advanced-topics-by-chin-hui-lee-engli/feed/ 0 https://acousticresearchspeakers.net/2024/04/human-and-automatic-speaker-recognition-over-telecommunication-channels-by-laura/ https://acousticresearchspeakers.net/2024/04/human-and-automatic-speaker-recognition-over-telecommunication-channels-by-laura/#comments Mon, 15 Apr 2024 18:49:43 +0000 https://acousticresearchspeakers.net/2024/04/human-and-automatic-speaker-recognition-over-telecommunication-channels-by-laura/ read more »]]>



Human and Automatic Speaker Recognition over Telecommunication Channels. By Laura Fernández Gallardo. This work addresses the evaluation of the human and the automatic speaker recognition performances under different channel distortions caused by bandwidth limitation, codecs, and electro-acoustic user interfaces, among other impairments. Its main contribution is the demonstration of the benefits of communication channels of extended bandwidth, together with an insight into how speaker-specific characteristics of speech are preserved through different transmissions. It provides sufficient motivation for considering speaker recognition as a criterion for the migration from narrowband to enhanced bandwidths, such as wideband and super-wideband. Grand Eagle Retail is the ideal place for all your shopping needs! We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. Please contact Customer Services and request “Return Authorisation” before you send your item back to us. We cannot take responsibility for items which are lost or damaged in transit. Home, Garden & Pets.

]]> https://acousticresearchspeakers.net/2024/04/human-and-automatic-speaker-recognition-over-telecommunication-channels-by-laura/feed/ 0