Sub-band based histogram equalization in cepstral domain for speech recognition

Joshi, Vikas; Bilgi, Raghvendra; Umesh, S.; García, Luz; Benítez, Carmen

doi:10.1016/j.specom.2015.02.005

Cited by 9 publications

(11 citation statements)

References 38 publications

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“…Relevant examples include level and/or frequency equalization techniques which attempt to transform the speech-plus-noise signal so that its features mimic a set of reference features calculated in the temporal, spectral, or cepstral domains. One line of work equalizes the noisy input signal to reflect the characteristics of the clean speech used to train the ASR system (Hilger and Ney, 2006;Joshi et al, 2011), while other work has focused on undoing the characteristics of Lombard speech (Boril and Hansen, 2010). Other techniques involve more complex models of intelligibility with the explicit goal of enhancing some intelligibility metric and may operate on clean speech prior to the addition of noise (Chanda and Park, 2007).…”

Section: B Comparison With Other Methodsmentioning

confidence: 99%

Masking release for hearing-impaired listeners: The effect of increased audibility through reduction of amplitude variability

Desloge

Reed

Braida

et al. 2017

The Journal of the Acoustical Society of America

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The masking release (i.e., better speech recognition in fluctuating compared to continuous noise backgrounds) observed for normal-hearing (NH) listeners is generally reduced or absent in hearing-impaired (HI) listeners. One explanation for this lies in the effects of reduced audibility: elevated thresholds may prevent HI listeners from taking advantage of signals available to NH listeners during the dips of temporally fluctuating noise where the interference is relatively weak. This hypothesis was addressed through the development of a signal-processing technique designed to increase the audibility of speech during dips in interrupted noise. This technique acts to (i) compare short-term and long-term estimates of energy, (ii) increase the level of short-term segments whose energy is below the average energy, and (iii) normalize the overall energy of the processed signal to be equivalent to that of the original long-term estimate. Evaluations of this energy-equalizing (EEQ) technique included consonant identification and sentence reception in backgrounds of continuous and regularly interrupted noise. For HI listeners, performance was generally similar for processed and unprocessed signals in continuous noise; however, superior performance for EEQ processing was observed in certain regularly interrupted noise backgrounds.

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Section: B Comparison With Other Methodsmentioning

confidence: 99%

Masking release for hearing-impaired listeners: The effect of increased audibility through reduction of amplitude variability

Desloge

Reed

Braida

et al. 2017

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

“…The work in [11] shows that S-HEQ outperforms the conventional CHN by providing better recognition accuracy. The overall procedure of S-HEQ is depicted in Figure 1.…”

Section: Brief Review Of S-heqmentioning

confidence: 99%

“…S-HEQ [11] offers additional insight into the possible distortions left unprocessed by CHN and a method for achieving even better noise robustness for speech features. In this section, we further examine S-HEQ to assess whether it can be further improved.…”

Section: Proposed Approach: Ws-heqmentioning

confidence: 99%

“…For example, the work in [11] revealed that in the CHN method, even though each cepstral channel is processed by histogram equalization (HEQ), a significant histogram mismatch still exists among the training and testing cepstral features for the low-pass filtered (LPF) and highpass filtered (HPF) portions of the intra-frame cepstra. Thus, the method of spatial HEQ in [11] further performs HEQ on the LPF and HPF portions to eliminate the aforementioned mismatch for the CHN-preprocessed cepstra. Compared with conventional CHN that processes each individual cepstral channel, spatial HEQ (S-HEQ) additionally takes the neighboring cepstral channels into consideration collectively and produces superior noise robustness.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, for a frame signal, the LPF and HPF portions of the cepstral vector just correspond to the logarithmic filter-bank (LFB) components at lower and higher frequencies, respectively. However, compensation performed directly on LPF and HPF is more helpful than that applied to the LFB components, most likely because the LFB components are significantly correlated [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intra-frame cepstral sub-band weighting and histogram equalization for noise-robust speech recognition

Hung

Fan

2013

J AUDIO SPEECH MUSIC PROC.

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In this paper, we propose a novel noise-robustness method known as weighted sub-band histogram equalization (WS-HEQ) to improve speech recognition accuracy in noise-corrupted environments. Considering the observations that high-and low-pass portions of the intra-frame cepstral features possess unequal importance for noise-corrupted speech recognition, WS-HEQ is intended to reduce the high-pass components of the cepstral features. Furthermore, we provide four types of WS-HEQ, which partially refers to the structure of spatial histogram equalization (S-HEQ). In the experiments conducted on the Aurora-2 noisy-digit database, the presented WS-HEQ yields significant recognition improvements relative to the Mel-scaled filter-bank cepstral coefficient (MFCC) baseline and to cepstral histogram normalization (CHN) in various noise-corrupted situations and exhibits a behavior superior to that of S-HEQ in most cases.

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A linear visual assessment tendency based clustering with power normalized cepstral coefficients for audio signal recognition system

Therese¹,

Lingam

2017

J Ambient Intell Human Comput

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Sub-band based histogram equalization in cepstral domain for speech recognition

Cited by 9 publications

References 38 publications

Masking release for hearing-impaired listeners: The effect of increased audibility through reduction of amplitude variability

Masking release for hearing-impaired listeners: The effect of increased audibility through reduction of amplitude variability

Intra-frame cepstral sub-band weighting and histogram equalization for noise-robust speech recognition

A linear visual assessment tendency based clustering with power normalized cepstral coefficients for audio signal recognition system

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