Denoised Bottleneck Features From Deep Autoencoders for Telephone Conversation Analysis

Automatic transcription of spoken documents is affected by automatic transcription errors that are especially frequent when speech is acquired in severe noisy conditions. Automatic speech recognition errors induce errors in the linguistic features used for a variety of natural language processing ta...

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Bibliographic Details
Published in:	IEEE/ACM transactions on audio, speech, and language processing Vol. 25; no. 9; pp. 1809 - 1820
Main Authors:	Janod, Killian, Morchid, Mohamed, Dufour, Richard, Linares, Georges, De Mori, Renato
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.09.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Acoustic noise Artificial Intelligence Automatic speech recognition Automatic speech recognition (ASR) Computer Science Conversation Conversation analysis Denoising denoisng autoencoders (DAEs) Document and Text Processing Feature extraction Feature recognition Manuals multilayer neural networks Natural language processing Noise measurement Noise reduction Recovery Representations Robustness Semantics Speech speech analytics Speech processing Speech recognition stacked autoencoders (SAEs) Transcription Voice recognition
ISSN:	2329-9290, 2329-9304
Online Access:	Get full text
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