Auditory ERB like admissible wavelet packet features for TIMIT phoneme recognition

Sahu, Prasanna Kumar; Biswas, Amit; Bhowmick, Anirban; Chandra, Mahesh

doi:10.1016/j.jestch.2014.04.004

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…Sahu et al (2014) and Biswas et al (2015), proposed a new set of acoustic features that also depend on wavelet packets called Wavelet packet based ERB (Equivalent Rectangular Bandwidth) Cepstral (WERBC). The main idea was to develop wavelet packet tree decomposition similar to the 24 sub-bands of the ERB filters (Sahu et al, 2014). Comparative study with baseline systems is presented to show the robustness of the proposed WERBC features.…”

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

confidence: 99%

“…The performance of the new feature is studied for the task of phoneme recognition. WERBC features have shown an overall improvement in recognition performance for English phoneme as compared to Wavelet like MFCC (WMFCC) (Sahu et al, 2014) and STFT based features. WERBC is found to be superior compared to the WMFCC, especially in case of noisy condition.…”

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

confidence: 99%

“…A high pre-emphasis filter is applied on the whole speech signal, then a frame size 24 msec with 14 msec overlap is used, windowed by Hamming widows, to derive cepstral features. To extract the low resonance component features, the whole frequency band is decomposed using ERB like WP decomposition proposed by Sahu et al (2014) as shown in Fig. 5.…”

Section: Resonance Wavelet Decomposition Cepstral Coefficients (Rwdcc)mentioning

confidence: 99%

See 2 more Smart Citations

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

confidence: 99%

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

confidence: 99%

Section: Resonance Wavelet Decomposition Cepstral Coefficients (Rwdcc)mentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Sahu, Astik Biswas, Anirban Bhowmick and Mahesh Chandra (2014) have discussed about auditory Equivalent Rectangular Bandwidth (ERB) based admissible wavelet packet features for TIMIT phoneme recognition [9]. The authors has introduced a new filter structure using admissible wavelet packet for English phoneme recognition.…”

Section: Related Workmentioning

confidence: 99%

Efficient Acoustic Front-End Processing for Tamil Speech Recognition using Modified GFCC Features

Vimala¹,

Radha²

2016

IJIGSP

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“…Hence, it is interesting to work with a new feature extraction technique based on human auditory system properties combined with speech masking property. Non-uniform WP sub-bands decomposition of the noisy speech y(t) = s(t) + n(t) is carried out using auditory ERB scale [4], [12], where s(t) is the clean signal, n(t) is the additive noise and t = 0, 1, ..., N − 1 is the time index. Then for each decomposed sub-band auditory masking threshold is calculated.…”

Section: Proposed Werbc-psw Feature Extractionmentioning

confidence: 99%

Acoustic feature extraction using ERB like wavelet sub-band perceptual Wiener filtering for noisy speech recognition

Biswas

Sahu

Bhowmick

et al. 2014

2014 Annual IEEE India Conference (INDICON)

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In the recent years Wavelet packet (WP) transform has been used as an important speech representation tool. WP based acoustic features have found to be more effective than short time Fourier transform (STFT) based features to capture the information of unvoiced phoneme in continuous speech. In this paper, a new 24 sub-band Equivalent Rectangular Bandwidth (ERB) like wavelet filter is proposed by employing perceptual wiener filter on each sub-band of decomposed noisy speech. Wiener filtered output is then proceed according to the Johnston model to calculate Auditory masking threshold for each wavelet decomposed sub-band. This threshold is used to design the perceptual sub-band weighting (PSW) filter. The output from each perceptually weighted sub-band is processed further to calculate acoustic front end features. This technique aims to enhance the noisy speech signal by using standard Wiener filter on psychoacoustically motivated decomposed wavelet sub-band by controlling the sub-band weighting factor. Hindi continuous digit database is used to evaluate the performance of the proposed feature. Obtained results show that proposed feature is effective for noisy speech recognition compared to some recently proposed feature extraction techniques.

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Auditory ERB like admissible wavelet packet features for TIMIT phoneme recognition

Cited by 16 publications

References 15 publications

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

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

Efficient Acoustic Front-End Processing for Tamil Speech Recognition using Modified GFCC Features

Acoustic feature extraction using ERB like wavelet sub-band perceptual Wiener filtering for noisy speech recognition

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