Abstract
In this paper, a feature extraction method for robust speech recognition in noisy environments is proposed. The proposed method is motivated by a biologically inspired auditory model which simulates the outer/middle ear filtering by a low-pass filter and the spectral behaviour of the cochlea by the Gammachirp auditory filterbank (GcFB). The speech recognition performance of our method is tested on speech signals corrupted by real-world noises. The evaluation results show that the proposed method gives better recognition rates compared to the classic techniques such as Perceptual Linear Prediction (PLP), Linear Predictive Coding (LPC), Linear Prediction Cepstral coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC). The used recognition system is based on the Hidden Markov Models with continuous Gaussian Mixture densities (HMM-GM).
Highlights
The Automatic speech recognition (ASR) system is one of the leading technologies acting on man–machine communication in real-world applications (Furui 2010)
We propose a biologically-inspired feature extraction method for robust recognition of noisy speech signals
A new auditory filter modelling-based feature extraction method for noisy speech recognition was presented in this paper
Summary
The Automatic speech recognition (ASR) system is one of the leading technologies acting on man–machine communication in real-world applications (Furui 2010). The feature extraction techniques are based on auditory filter modelling which uses a filterbank to The cochlear filter is modelled by a gammachirp auditory filterbank consisting of 34 filters, where the centre frequencies are spaced on the ERB-rate scale from 50 Hz to 8 kHz. The HTK 3.4.1 toolkit is exploited in the Model training and recognition of speech signals.
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