Mel scaled M-band wavelet filter bank for speech recognition

Upadhyaya, Prashant; Farooq, Omar; Abidi, Musiur Raza

doi:10.1007/s10772-018-9545-2

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The approximation error of any given scale can be analysed in terms of absolute value of deviations in the center frequencies and bandwidths between the original Bark scale and the approximated scale of each channel, absolute relative bandwidth deviation (RBD) of each channel and root mean square bandwidth deviation (RMSBD). RBD(k) and RM SBD are computed as [28]…”

Section: Illustrationmentioning

confidence: 99%

An Improved Rational Approximation of Bark Scale Using Low Complexity and Low Delay Filter Banks

Hareesh,

Bindiya

2024

Circuits Syst Signal Process

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This paper proposes an algorithm to obtain the sampling factors to model any frequency partitioning so that it is realized using low complexity rational decimated non-uniform filter banks (RDNUFBs). The proposed algorithm is employed to approximate the Bark scale to find a rational frequency partitioning that can be realized using RDNUFBs with less approximation error. The proposed Bark frequency partitioning is found to reduce the average deviation in center frequency and bandwidth by 65.04% and 48.50% respectively, and root mean square bandwidth deviation by 54.46% when compared to those of the existing perceptual wavelet approximation of Bark scale.In the first filter bank design approach discussed in the paper, a near perfect reconstruction partially cosine modulated filter bank is employed to obtain the improved Bark frequency partitioning. In the second design approach, the hardware complexity of the PCM based RDNUFB is reduced by deriving the channels with different sampling factors from the same prototype filter by the method of channel merging. There is a considerable reduction of 40.83% in the number of multipliers when merging of partially cosine modulated channels is employed. It is also found that both the proposed approaches can reduce the delay by 95.17% when compared to the existing rational tree approximation methods and hence, are suitable for real time applications.

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Section: Illustrationmentioning

confidence: 99%

An Improved Rational Approximation of Bark Scale Using Low Complexity and Low Delay Filter Banks

Hareesh,

Bindiya

2024

Circuits Syst Signal Process

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“…The performance of any ASR system is evaluated in terms of word error rate and word recognition accuracy [24] given by equations (20) and (21) respectively.…”

Section: Performance Analysismentioning

confidence: 99%

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

2019

ijrte

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A digit recognition system is built for recognizing the sequence of digits through 0-9. The system is experimented with speech corpus created in the room environment. The acoustic information to feature representation is achieved using PLP and MFCC features. The system initially utilized the conventional GMM-HMM framework, state of the art hybrid classifier with varied number of states to complete the speech recognition task, i.e., the system is first trained and tested using Monophone models, and system’s recognition accuracy is then evaluated using Triphone Models: Triphone1 models, which was later followed by Triphones2 models and Triphones3 Models. The Ngram Language model is used for both Monophone and Triphone training. The system performance is evaluated with the use of MFCC and PLP parameterisation techniques on Kaldi toolkit. The system performance is evaluated using metrics word error rate (WER) and Word Recognition Accuracy (WRA). The proposed system can be utilized for building speech applications

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“…Experiments have shown that the classification of phonemes using selected wavelet features surpasses the best acoustic features, especially in the noisy environments. Upadhyaya et al (2018) proposed using a Mel scaled M-band wavelet filter bank structure to extract robust acoustic features for speech recognition application. Results (Biswas et al, 2016) shows that the proposed feature extraction from the proposed filter bank shows an improvement in terms of Word Recognition Accuracy (WRA) at all SNR range (20-0 dB) over baseline (MFCC) features.…”

Section: Corresponding Author: Ihsan Al-hassani Department Of Telecomentioning

confidence: 99%

A New Robust Resonance Based Wavelet Decomposition Cepstral Features for Phoneme Recoszgnition

Al-Hassani¹,

Al-Dakkak²,

Assami³

2019

Res. J. Applied Sci.

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Robust Automatic Speech Recognition (ASR) is a challenging task that has been an active research subject for the last 20 years. And still results are very modest in the highly noisy environments. In this study, we propose a new speech parameterization method based on concatenating two wavelet packet decompositions, one decomposition using low Q-factor wavelet and another with high Q-factor wavelet, to extract speech features suitable for ASR task in noisy conditions. Experiments on TIMIT dataset for phonemes recognition show that the proposed wavelet-based features outperform MFCC in all noisy conditions.

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Mel scaled M-band wavelet filter bank for speech recognition

Cited by 6 publications

References 42 publications

An Improved Rational Approximation of Bark Scale Using Low Complexity and Low Delay Filter Banks

An Improved Rational Approximation of Bark Scale Using Low Complexity and Low Delay Filter Banks

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

A New Robust Resonance Based Wavelet Decomposition Cepstral Features for Phoneme Recoszgnition

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