A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

MUSTAFA, Mohammed, ALLEN, Tony and APPIAH, Kofi (2017). A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural Computing and Applications.

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Official URL: https://link.springer.com/article/10.1007%2Fs00521...
Link to published version:: https://doi.org/10.1007/s00521-017-3028-2
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    Abstract

    The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic Multi-Layer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques.

    Item Type: Article
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
    Identification Number: https://doi.org/10.1007/s00521-017-3028-2
    Depositing User: Kofi Appiah
    Date Deposited: 17 Jan 2018 16:21
    Last Modified: 16 Nov 2018 13:51
    URI: http://shura.shu.ac.uk/id/eprint/18383

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