{"id":155944,"date":"2006-11-01T00:00:00","date_gmt":"2006-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/subband-likelihood-maximizing-beamforming-for-speech-recognition-in-reverberant-environments\/"},"modified":"2018-10-16T20:20:40","modified_gmt":"2018-10-17T03:20:40","slug":"subband-likelihood-maximizing-beamforming-for-speech-recognition-in-reverberant-environments","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/subband-likelihood-maximizing-beamforming-for-speech-recognition-in-reverberant-environments\/","title":{"rendered":"Subband Likelihood-Maximizing Beamforming for Speech Recognition in Reverberant Environments"},"content":{"rendered":"<div class=\"asset-content\">\n<div class=\"asset-content\">\n<p>In this paper, we introduce Subband LIkelihood MAximizing BEAMforming (S-LIMABEAM), a new microphone-array processing algorithm specifically designed for speech recognition applications. The proposed algorithm is an extension of the previously developed LIMABEAM array processing algorithm. Unlike most array processing algorithms which operate according to some waveform-level objective function, the goal of LIMABEAM is to find the set of array parameters that maximizes the likelihood of the correct recognition hypothesis. Optimizing the array parameters in this manner results in significant improvements in recognition accuracy over conventional array processing methods when speech is corrupted by additive noise and moderate levels of reverberation. Despite the success of the LIMABEAM algorithm in such environments, little improvement was achieved in highly reverberant environments. In such situations where the noise is highly correlated to the speech signal and the number of filter parameters to estimate is large, subband processing has been used to improve the performance of LMS-type adaptive filtering algorithms. We use subband processing principles to design a novel array processing architecture in which select groups of subbands are processed jointly to maximize the likelihood of the resulting speech recognition features, as measured by the recognizer itself. By creating a subband filtering architecture that explicitly accounts for the manner in which recognition features are computed, we can effectively apply the LIMABEAM framework to highly reverberant environments. By doing so, we are able to achieve improvements in word error rate of over 20% compared to conventional methods in highly reverberant environments.<\/p>\n<\/div>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we introduce Subband LIkelihood MAximizing BEAMforming (S-LIMABEAM), a new microphone-array processing algorithm specifically designed for speech recognition applications. The proposed algorithm is an extension of the previously developed LIMABEAM array processing algorithm. Unlike most array processing algorithms which operate according to some waveform-level objective function, the goal of LIMABEAM is to find [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-155944","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_edition":"","msr_affiliation":"","msr_published_date":"2006-11-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"2109-2121","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Transactions on Audio, Speech and Language Processing","msr_volume":"14","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"6","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"227830","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"2006-mseltzer-trans.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2006\/11\/2006-mseltzer-trans.pdf","id":227830,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":227830,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2006\/11\/2006-mseltzer-trans.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"mseltzer","user_id":33017,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mseltzer"},{"type":"text","value":"Richard M. Stern","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169715],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"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. If you are&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169715"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/155944"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/155944\/revisions"}],"predecessor-version":[{"id":526510,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/155944\/revisions\/526510"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=155944"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=155944"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=155944"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=155944"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=155944"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=155944"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=155944"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=155944"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=155944"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=155944"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=155944"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=155944"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=155944"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=155944"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=155944"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=155944"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}