{"id":162724,"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\/application-of-e%ce%b1nets-to-feature-recognitionof-articulation-manner-in-knowledge-based-automatic-speech-recognition\/"},"modified":"2018-10-16T20:52:18","modified_gmt":"2018-10-17T03:52:18","slug":"application-of-e%ce%b1nets-to-feature-recognitionof-articulation-manner-in-knowledge-based-automatic-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/application-of-e%ce%b1nets-to-feature-recognitionof-articulation-manner-in-knowledge-based-automatic-speech-recognition\/","title":{"rendered":"Application of E\u00ce\u00b1Nets to feature recognitionof articulation manner in knowledge-based automatic speech recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Speech recognition has become common in many application<br \/>\ndomains. Incorporating acoustic-phonetic knowledge into Automatic<br \/>\nSpeech Recognition (ASR) systems design has been proven a viable approach<br \/>\nto rise ASR accuracy. Manner of articulation attributes such as<br \/>\nvowel, stop, fricative, approximant, nasal, and silence are examples of<br \/>\nsuch knowledge. Neural networks have already been used successfully as<br \/>\ndetectors for manner of articulation attributes starting from representations<br \/>\nof speech signal frames. In this paper, a set of six detectors for the<br \/>\nabove mentioned attributes is designed based on the E-\u03b1Net model of<br \/>\nneural networks. This model was chosen for its capability to learn hidden<br \/>\nactivation functions that results in better generalization properties. Experimental<br \/>\nset-up and results are presented that show an average 3.5%<br \/>\nimprovement over a baseline neural network implementation.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Speech recognition has become common in many application domains. Incorporating acoustic-phonetic knowledge into Automatic Speech Recognition (ASR) systems design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as [&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":[{"type":"user_nicename","value":"jinyli"}],"msr_publishername":"Springer","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Lecture Notes in Computer Science","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":"","msr_conference_name":"Lecture Notes in Computer 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