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A bio-inspired feature extraction for robust speech recognition.

Zouhir Y, Ouni K - Springerplus (2014)

Bottom Line: 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 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).

View Article: PubMed Central - PubMed

Affiliation: Research Unit: Signals and Mechatronic Systems, SMS, Higher School of Technology and Computer Science (ESTI), University of Carthage, Carthage, Tunisia.

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).

No MeSH data available.


The Markov Model with 5 states simple model (Young et al. 2009).
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Fig2: The Markov Model with 5 states simple model (Young et al. 2009).

Mentions: In HMM based ASR, the sequence of observed acoustic vectors (O = o1, o2, o3, …, ot, … oT, where ot is the acoustic vector observed at time t) associated to each word is modelled as being generated by a Markov Model (Young et al. 2009) as shown in Figure 2.Figure 2


A bio-inspired feature extraction for robust speech recognition.

Zouhir Y, Ouni K - Springerplus (2014)

The Markov Model with 5 states simple model (Young et al. 2009).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230714&req=5

Fig2: The Markov Model with 5 states simple model (Young et al. 2009).
Mentions: In HMM based ASR, the sequence of observed acoustic vectors (O = o1, o2, o3, …, ot, … oT, where ot is the acoustic vector observed at time t) associated to each word is modelled as being generated by a Markov Model (Young et al. 2009) as shown in Figure 2.Figure 2

Bottom Line: 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 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).

View Article: PubMed Central - PubMed

Affiliation: Research Unit: Signals and Mechatronic Systems, SMS, Higher School of Technology and Computer Science (ESTI), University of Carthage, Carthage, Tunisia.

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).

No MeSH data available.