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Multi-level adaptive networks in tandem and hybrid ASR systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6975-6979
Number of pages5
ISBN (Print)978-1-4799-0356-6
DOIs
Publication statusPublished - 2013

Abstract

In this paper we investigate the use of Multi-level adaptive networks (MLAN) to incorporate out-of-domain data when training large vocabulary speech recognition systems. In a set of experiments on multi-genre broadcast data and on TED lecture recordings we present results using of out-of-domain features in a hybrid DNN system and explore tandem systems using a variety of input acoustic features. Our experiments indicate using the MLAN approach in both hybrid and tandem systems results in consistent reductions in word error rate of 5-10% relative.

    Research areas

  • error analysis, speech recognition, vocabulary, TED lecture recordings, hybrid ASR systems, multigenre broadcast data, multilevel adaptive networks, out-of-domain data, vocabulary speech recognition systems, word error rate, Acoustics, Adaptation models, Hidden Markov models, Neural networks, Speech, Speech recognition, Training, BBC, MLAN, TED, deep neural networks, hybrid, tandem

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