Next Article in Journal
Position Certainty Propagation: A Localization Service for Ad-Hoc Networks
Previous Article in Journal
Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle
Computers 2019, 8(1), 5;

Robust Cochlear-Model-Based Speech Recognition

Laboratory for Smart Environment Technologies, FESB, University of Split, R. Boskovica 32, 21000 Split, Croatia
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
PDF [491 KB, uploaded 1 January 2019]


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Russo, M.; Stella, M.; Sikora, M.; Pekić, V. Robust Cochlear-Model-Based Speech Recognition. Computers 2019, 8, 5.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Computers EISSN 2073-431X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top