{"id":164887,"date":"2000-01-01T00:00:00","date_gmt":"2000-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/robustness-to-telephone-handset-distortion-in-speaker-recognition-by-discriminative-feature-design\/"},"modified":"2018-10-16T20:11:53","modified_gmt":"2018-10-17T03:11:53","slug":"robustness-to-telephone-handset-distortion-in-speaker-recognition-by-discriminative-feature-design","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/robustness-to-telephone-handset-distortion-in-speaker-recognition-by-discriminative-feature-design\/","title":{"rendered":"Robustness to Telephone Handset Distortion in Speaker Recognition by Discriminative Feature Design"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A deep neural network (deep learning) method is described for designing speaker recognition features that are robust to telephone handset distortion. The approach transforms features such as mel-cepstral features, log spectrum, and prosody-based features with a non-linear artificial neural network. The neural network is discriminatively trained to maximize speaker recognition performance specifically in the setting of telephone handset mismatch between training and testing. The algorithm requires neither stereo recordings of speech during training nor manual labeling of handset types either in training or testing. Results on the 1998 National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation corpus show relative improvements as high as 28% for the new multilayered perceptron (MLP)-based features as compared to a standard mel-cepstral feature set with cepstral mean subtraction (CMS) and handset-dependent normalizing impostor models.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A deep neural network (deep learning) method is described for designing speaker recognition features that are robust to telephone handset distortion. The approach transforms features such as mel-cepstral features, log spectrum, and prosody-based features with a non-linear artificial neural network. The neural network is discriminatively trained to maximize speaker recognition performance specifically in the setting [&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":"Elsevier","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Speech Communication","msr_number":"","msr_organization":"","msr_pages_string":"181-192","msr_page_range_start":"181","msr_page_range_end":"192","msr_series":"","msr_volume":"31","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2000-01-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2000,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13554],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-164887","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"Elsevier","msr_edition":"","msr_affiliation":"","msr_published_date":"2000-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"181-192","msr_chapter":"","msr_isbn":"","msr_journal":"Speech Communication","msr_volume":"31","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","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":"225232","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"1-s2.0-S0167639399000771-main.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2000\/01\/1-s2.0-S0167639399000771-main.pdf","id":225232,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":225232,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2000\/01\/1-s2.0-S0167639399000771-main.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"lheck","user_id":32659,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=lheck"},{"type":"text","value":"Yochai Konig","user_id":0,"rest_url":false},{"type":"text","value":"M. Kemal Sonmez","user_id":0,"rest_url":false},{"type":"text","value":"Mitch Weintraub","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144778],"msr_project":[169434,169832,169715],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":169434,"post_title":"Acoustic Modeling","post_name":"acoustic-modeling","post_type":"msr-project","post_date":"2004-01-29 16:42:42","post_modified":"2019-08-14 14:50:04","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/acoustic-modeling\/","post_excerpt":"Acoustic modeling of speech typically refers to the process of\u00a0establishing statistical\u00a0representations for the feature vector sequences\u00a0computed from the speech waveform. Hidden Markov Model (HMM) is one most common type of acoustuc models. 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":169832,"post_title":"Speaker Identification (WhisperID)","post_name":"speaker-identification-whisperid","post_type":"msr-project","post_date":"2004-01-29 16:52:18","post_modified":"2017-06-19 09:22:47","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/speaker-identification-whisperid\/","post_excerpt":"When you speak to someone, they don't just recognize what you say: they recognize who you are. WhisperID will let computers do that, too, figuring out who you are by the way you sound. Home PC Security. In your home, Speaker Identification will make it easier for you to log into your computer, just by saying \"Log me in!\" Office PC Security. In your office, Speaker ID can add an extra level of protection to&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169832"}]}},{"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\/164887","targetHints":{"allow":["GET"]}}],"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\/164887\/revisions"}],"predecessor-version":[{"id":524234,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/164887\/revisions\/524234"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=164887"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=164887"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=164887"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=164887"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=164887"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=164887"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=164887"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=164887"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=164887"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=164887"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=164887"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=164887"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=164887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}