Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition

Liang Lu, Steve Renals

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

Abstract / Description of output

For speech recognition, deep neural networks (DNNs) have significantly improved the recognition accuracy in most of benchmark datasets and application domains. However, compared to the conventional Gaussian mixture models, DNN-based acoustic models usually have much larger number of model parameters, making it challenging for their applications in resource constrained platforms, e.g., mobile devices. In this paper, we study the application of the recently proposed highway network to train small-footprint DNNs, which are {\it thinner} and {\it deeper}, and have significantly smaller number of model parameters compared to conventional DNNs. We investigated this approach on the AMI meeting speech transcription corpus which has around 70 hours of audio data. The highway neural networks constantly outperformed their plain DNN counterparts, and the number of model parameters can be reduced significantly without sacrificing the recognition accuracy.
Original languageEnglish
Title of host publicationProceedings of Interspeech 2016
Place of PublicationSan Francisco, United States
Number of pages5
Publication statusPublished - 12 Sept 2016
EventInterspeech 2016 - San Francisco, United States
Duration: 8 Sept 201612 Sept 2016

Publication series

PublisherInternational Speech Communication Association
ISSN (Print)1990-9772


ConferenceInterspeech 2016
Country/TerritoryUnited States
CitySan Francisco
Internet address


Dive into the research topics of 'Small-footprint Deep Neural Networks with Highway Connections for Speech Recognition'. Together they form a unique fingerprint.

Cite this