In this paper, we propose a novel discriminative training approach to spoken utterance classification (SUC). The ultimate objective of the SUC task, originally developed to map a spoken speech utterance into the most appropriate semantic class, is to minimize the classification error rate (CER). Conventionally, a two-phase approach is adapted, in which the first phase is the ASR transcription phase, and the second phase is the semantic classification phase. In the proposed framework, the classification error rate is approximated as differentiable functions of the language and classifier model parameters. Furthermore, in order to exploit all the available information from the first phase, class-specific discriminant functions are defined based on score functions derived from the N-best lists. Our experimental results on the standard ATIS database indicate a notable reduction in CER from the earlier best result on the identical task. The proposed framework achieved a reduction of CER from 4.92% to 4.04%.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose a novel discriminative training approach to spoken utterance classification (SUC). The ultimate objective of the SUC task, originally developed to map a spoken speech utterance into the most appropriate semantic class, is to minimize the classification error rate (CER). Conventionally, a two-phase approach is adapted, in which the first phase […]<\/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":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. of the International Conference on Acoustics, Speech and Signal Processing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"IV-5-IV-8","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"4","msr_copyright":"\u00a9 2007 IEEE. Personal use of this material is permitted. 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The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods. Projects Deeper Understanding: Moving\u00a0beyond shallow targeted understanding towards building domain independent SLU models. 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Intent understanding is about identifying the action a user wants a computer to take or the information she\/he would like to obtain, conveyed in a spoken utterance or…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170147"}]}},{"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…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169434"}]}},{"ID":169630,"post_title":"Language Modeling for Speech Recognition","post_name":"language-modeling-for-speech-recognition","post_type":"msr-project","post_date":"2004-01-29 16:43:32","post_modified":"2019-08-19 09:41:10","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/language-modeling-for-speech-recognition\/","post_excerpt":"Did I just say \"It's fun to recognize speech?\" or \"It's fun to wreck a nice beach?\" It's hard to tell because they sound about the same. 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