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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)
Number of pages5
ISBN (Print)978-1-4799-0356-6
Publication statusPublished - 2013


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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ID: 11805098