In this paper, we present our recent development of a modeldomain environment-robust adaptation algorithm, which demonstrates high performance in the standard Aurora 2 speech recognition task. The algorithm consists of two main steps. First, the noise and channel parameters are estimated using a nonlinear environment distortion model in the cepstral domain, the speech recognizer\u2019s \u201cfeedback\u201d information, and the Vector-Taylor-Series (VTS) linearization technique collectively. Second, the estimated noise and channel parameters are used to adapt the static and dynamic portions of the HMM means and variances. This two-step algorithm enables Joint compensation of both Additive and Convolutive distortions (JAC).<\/p>\n
In the experimental evaluation using the standard Aurora 2 task, the proposed JAC\/VTS algorithm achieves 91.11% accuracy using the clean-trained simple HMM backend as the baseline system for the model adaptation. This represents high recognition performance on this task without discriminative training of the HMM system. Detailed analysis on the experimental results shows that adaptation of the dynamic portion of the HMM mean and variance parameters is critical to the success of our algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we present our recent development of a modeldomain environment-robust adaptation algorithm, which demonstrates high performance in the standard Aurora 2 speech recognition task. The algorithm consists of two main steps. First, the noise and channel parameters are estimated using a nonlinear environment distortion model in the cepstral domain, the speech recognizer\u2019s \u201cfeedback\u201d […]<\/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":[{"type":"user_nicename","value":"jinyli","user_id":"32312"},{"type":"user_nicename","value":"deng","user_id":"31602"},{"type":"user_nicename","value":"dongyu","user_id":"31667"},{"type":"user_nicename","value":"ygong","user_id":"34994"},{"type":"user_nicename","value":"alexac","user_id":"30932"}],"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"Proceedings IEEE Workshop on ASRU","msr_chapter":"","msr_edition":"Proceedings IEEE Workshop on ASRU","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 2007 IEEE. 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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…","_links":{"self":[{"href":"https:\/\/cm-edgetun.pages.dev\/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:\/\/cm-edgetun.pages.dev\/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. 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