{"id":168304,"date":"2015-04-01T00:00:00","date_gmt":"2015-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/speech-recognition-with-prediction-adaptation-correction-recurrent-neural-networks\/"},"modified":"2018-10-16T20:14:09","modified_gmt":"2018-10-17T03:14:09","slug":"speech-recognition-with-prediction-adaptation-correction-recurrent-neural-networks","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/speech-recognition-with-prediction-adaptation-correction-recurrent-neural-networks\/","title":{"rendered":"Speech Recognition with Prediction-Adaptation-Correction Recurrent Neural Networks"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We propose the prediction-adaptation-correction RNN (PAC-RNN), in which a correction DNN estimates the state posterior probability based on both the current frame and the prediction made on the past frames by a prediction DNN. The result from the main DNN is fed back to the prediction DNN to make better predictions for the future frames. In the PAC-RNN, we can consider that, given the new, current frame information, the main DNN makes a correction on the prediction made by the prediction DNN. Alternatively, it can be viewed as adapting the main DNN\u2019s behavior based on the prediction DNN\u2019s prediction. Experiments on the TIMIT phone recognition task indicate that the PAC-RNN outperforms DNN, RNN, and LSTM with 2.4%, 2.1%, and 1.9% absolute phone accuracy improvement, respectively. We found that incorporating the prediction objective and including the recurrent loop are both important to boost the performance of the PAC-RNN.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose the prediction-adaptation-correction RNN (PAC-RNN), in which a correction DNN estimates the state posterior probability based on both the current frame and the prediction made on the past frames by a prediction DNN. The result from the main DNN is fed back to the prediction DNN to make better predictions for the future frames. [&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":"IEEE - Institute of Electrical and Electronics Engineers","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","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 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting\/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Yu Zhang, Michael L. 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