Techniques for Noise Robustness in Automatic Speech Recognition (Hardcover)

Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems.  As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences.

Key features:

• Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech.
• Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments.
• Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR.
• Includes contributions from top ASR researchers from leading research units in the field

自動語音識別（ASR）系統在日常生活中的應用越來越廣泛。許多常見的使用環境都存在噪音，例如使用者在繁忙的餐廳或街道上使用語音搜索系統。這可能導致語音錄音品質下降，並對語音識別系統的性能產生不利影響。隨著ASR系統的使用增加，了解處理這些問題的最新技術對系統和應用工程師以及從事ASR技術的研究人員至關重要。本書全面介紹了用於提高語音識別系統對這些外部干擾的韌性的技術的最新發展。

主要特點：
- 綜述了所有主要的抗噪聲ASR方法，包括信號分離、語音活動檢測、韌性特徵提取、模型補償和適應、缺失數據技術以及對混響語音的識別。
- 在ASR技術在具有挑戰性環境中的更廣泛應用的背景下，及時闡述了該主題。
- 解決了韌性問題和信號退化，這是ASR從業人員的關鍵需求。
- 包括來自該領域領先研究單位的頂尖ASR研究人員的貢獻。