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Cochlea-inspired speech recognition interface

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Abstract

Automatic speech recognition (ASR) technology provides a natural interface for human-machine interaction. Typical ASR systems can achieve high performance in quiet environments but, unlike humans, perform poorly in real-world situations. To better simulate the human auditory periphery and improve the performance in realistic noisy scenarios, we propose two models of speech recognition front-ends based on a biophysical cochlear model. The first front-end is based on the method of signal reconstruction from a basilar membrane response. When applied to noisy speech, this method results in improved signal quality. This method can be used as a preprocessing step in a standard ASR system and can also be used as a noise reduction technique for other applications. The second front-end we propose is based on the construction of speech recognition coefficients directly from a basilar membrane response. Experimental results using a continuous-density hidden Markov model (HMM) recognizer demonstrate significant improvement in performance compared to standard Mel-frequency cepstral coefficients (MFCC) in various types of noisy conditions.

Speech recognition model based on cochlear front-end

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Funding

This work has been fully supported by the Croatian Science Foundation under project number UIP-2014-09-3875.

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Authors and Affiliations

  1. Laboratory for Smart Environment Technologies, FESB - University of Split, Split, Croatia

    Mladen Russo, Maja Stella, Marjan Sikora & Matko Šarić

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  1. Mladen Russo
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  2. Maja Stella
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  4. Matko Šarić
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Correspondence to Mladen Russo.

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Russo, M., Stella, M., Sikora, M. et al. Cochlea-inspired speech recognition interface. Med Biol Eng Comput 57, 1393–1403 (2019). https://doi.org/10.1007/s11517-019-01963-6

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