{"id":156413,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/use-of-temporal-codes-computed-from-a-cochlear-model-for-speech-recognition\/"},"modified":"2018-10-16T21:31:14","modified_gmt":"2018-10-17T04:31:14","slug":"use-of-temporal-codes-computed-from-a-cochlear-model-for-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/use-of-temporal-codes-computed-from-a-cochlear-model-for-speech-recognition\/","title":{"rendered":"Use of Temporal Codes Computed from a Cochlear Model for Speech Recognition"},"content":{"rendered":"<p>The human species is largely defined by its use of spoken language, so integral is speech communication to behavior and social interaction. Despite its importance in everyday life, comparatively little is known about the auditory mechanisms that underlie the ability to understand language. The current volume examines the perception and processing of speech from the perspective of the hearing system. The chapters in this book describe a comprehensive set of approaches to the scientific study of speech and hearing, ranging from anatomy and physiology, to psychophysics and perception, and computational modeling. The auditory basis of speech is examined within a biological and an evolutionary context, and its relevance to applied domains such as communication disorders and speech technology discussed in detail. This volume will be of interest to scientists, engineers, and clinicians whose professional work pertains to any aspect of spoken language or hearing science.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The human species is largely defined by its use of spoken language, so integral is speech communication to behavior and social interaction. Despite its importance in everyday life, comparatively little is known about the auditory mechanisms that underlie the ability to understand language. The current volume examines the perception and processing of speech from the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Lawrence Erlbaum Associates, Inc.","msr_publisher_other":"","msr_booktitle":"Listening to Speech: An Auditory Perspective","msr_chapter":"15","msr_edition":"Chapter 15, S. Greenberg and W. Ainsworth (eds.) 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Other acosutic models include segmental models, super-segmental models (including hidden dynamic models), neural networks, maximum entropy models, and (hidden) conditional random fields, etc. Acoustic modeling also encompasses \"pronunciation modeling\", which describes how a sequence or multi-sequences of fundamental speech units\u00a0(such as phones or&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169434"}]}},{"ID":169715,"post_title":"Noise Robust Speech Recognition","post_name":"noise-robust-speech-recognition","post_type":"msr-project","post_date":"2002-02-19 14:36:52","post_modified":"2017-06-02 09:12:19","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/noise-robust-speech-recognition\/","post_excerpt":"Techniques to improve the robustness of automatic speech recognition systems to noise and channel mismatches Robustness of ASR Technology to Background Noise You have probably seen that most people using a speech dictation software are wearing a close-talking microphone. So, why has senior researcher Li Deng been trying to get rid of close-talking microphones? Close-talking microphones pick up relatively little background noise and speech recognition systems can obtain decent accuracy with them. 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