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Computers 2019, 8(1), 5; https://doi.org/10.3390/computers8010005

Robust Cochlear-Model-Based Speech Recognition

Laboratory for Smart Environment Technologies, FESB, University of Split, R. Boskovica 32, 21000 Split, Croatia
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 14 October 2018 / Revised: 21 December 2018 / Accepted: 23 December 2018 / Published: 1 January 2019
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Abstract

Accurate speech recognition can provide a natural interface for human–computer interaction. Recognition rates of the modern speech recognition systems are highly dependent on background noise levels and a choice of acoustic feature extraction method can have a significant impact on system performance. This paper presents a robust speech recognition system based on a front-end motivated by human cochlear processing of audio signals. In the proposed front-end, cochlear behavior is first emulated by the filtering operations of the gammatone filterbank and subsequently by the Inner Hair cell (IHC) processing stage. Experimental results using a continuous density Hidden Markov Model (HMM) recognizer with the proposed Gammatone Hair Cell (GHC) coefficients are lower for clean speech conditions, but demonstrate significant improvement in performance in noisy conditions compared to standard Mel-Frequency Cepstral Coefficients (MFCC) baseline. View Full-Text
Keywords: speech recognition; cochlea; Gammatone filterbank; IHC; HMM speech recognition; cochlea; Gammatone filterbank; IHC; HMM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Russo, M.; Stella, M.; Sikora, M.; Pekić, V. Robust Cochlear-Model-Based Speech Recognition. Computers 2019, 8, 5.

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