Automatic speech recognition (ASR) systems are findingincreasing use in everyday life. Many of the commonplaceenvironments where the systems are used are noisy, for exampleusers calling up a voice search system from a busy cafeteria or astreet. This can result in degraded speech recordings and adverselyaffect the performance of speech recognition systems. As theuse of ASR systems increases, knowledge of the state-of-the-art intechniques to deal with such problems becomes critical to systemand application engineers and researchers who work with or on ASRtechnologies. This book presents a comprehensive survey of thestate-of-the-art in techniques used to improve the robustness ofspeech recognition systems to these degrading externalinfluences.
Key features:
* Reviews all the main noise robust ASR approaches, includingsignal separation, voice activity detection, robust featureextraction, model compensation and adaptation, missing datatechniques and recognition of reverberant speech.
* Acts as a timely exposition of the topic in light of morewidespread use in the future of ASR technology in challengingenvironments.
* Addresses robustness issues and signal degradation which areboth key requirements for practitioners of ASR.
* Includes contributions from top ASR researchers from leadingresearch units in the field