Robust Feature Extraction of Speech Via Noise Reduction in Autocorrelation Domain

Farahani, Gholamreza; Ahadi, Seyed Mohammad; Homayounpour, Mohammad Mehdi

doi:10.1007/11848035_62

Cited by 12 publications

(16 citation statements)

References 7 publications

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“…Here, in place of removing the lower lag autocorrelation components of the noisy signal, we try to estimate the noise autocorrelation sequence and deduct it from the noisy signal autocorrelation sequence. This is conceptually similar to the well-known spectral subtraction with the exception that it is not magnitude spectrum, but to the autocorrelation sequence [17]. An instant advantage is that there is no need to deal with phase issue.…”

Section: Autocorrelation-based Noise Subtraction (Ans)mentioning

confidence: 94%

“…its inability to deal with noises that have autocorrelation components spread out over different lags [17].…”

Section: Spectral Peaks Of Filtered Higher-lag Autocorrelation Sequenmentioning

confidence: 99%

“…In reference [17], the average autocorrelation of a number of non-speech frames of the utterance is used as an estimate of the noise autocorrelation sequence. We write this as where P is the number of non-speech frames of the utterance used and is the noise autocorrelation estimate.…”

Section: Autocorrelation-based Noise Subtraction (Ans)mentioning

confidence: 99%

“…Clearly, with increasing SNR, the values of α should decrease and vice versa. The trend of this change was set to linear, as shown in the figure, according to changes observed in system recognition performance in practice [17]. We tested the proposed method with/without taking into account the signal/noise cross correlation.…”

Section: Considering Overestimation Parametermentioning

confidence: 99%

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Robust Feature Extraction Using Autocorrelation Domain for Noisy Speech Recognition

Farahani¹

2017

SIPIJ

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show abstract

Section: Autocorrelation-based Noise Subtraction (Ans)mentioning

confidence: 94%

“…its inability to deal with noises that have autocorrelation components spread out over different lags [17].…”

Section: Spectral Peaks Of Filtered Higher-lag Autocorrelation Sequenmentioning

confidence: 99%

Section: Autocorrelation-based Noise Subtraction (Ans)mentioning

confidence: 99%

Section: Considering Overestimation Parametermentioning

confidence: 99%

See 2 more Smart Citations

Robust Feature Extraction Using Autocorrelation Domain for Noisy Speech Recognition

Farahani¹

2017

SIPIJ

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“…Secondly, we should take the cointegration test [7] 2011 International Conference on Management Science & Engineering (18 th ) September [13][14][15]2011 Rome, Italy on the time sequences. The cointegration test is suitable for the nonstationary series, which is integrated of one order [6] , in order to prove that there is the long-run equilibrium relationship between the all series.…”

Section: Introduction Of Co-integration Theorymentioning

confidence: 99%

An empirical research on the relation between the air transportation and economic growth

Huiyun

Mei-gui

2011

2011 International Conference on Management Science &Amp; Engineering 18th Annual Conference Proceedings

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Civil aviation transportation industry is an important department of social economy and plays an important role in the development of economy. The relationship of the development of civil aviation and economic growth has become research focus in research and industry field. This paper is based on the cointegration theory in econometrics, through establishing the long-run equilibrium equation and the error correction model, and adopting the statistics of Gross Domestic Product and the total turnover of air transport, in order to inculcate an empirical study on the relationship between the air transport and economic growth. The results show that economic growth can significantly promote the development of civil aviation, on the contrary, the development of civil aviation has a strong catalytic role on economic growth. Finally, the paper analyzes the reasons for the results, and gives some recommendations.

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Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

Bawa

Kadyan

Tripathy³

et al. 2022

Complex Intell. Syst.

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Development of a native language robust ASR framework is very challenging as well as an active area of research. Although an urge for investigation of effective front-end as well as back-end approaches are required for tackling environment differences, large training complexity and inter-speaker variability in achieving success of a recognition system. In this paper, four front-end approaches: mel-frequency cepstral coefficients (MFCC), Gammatone frequency cepstral coefficients (GFCC), relative spectral-perceptual linear prediction (RASTA-PLP) and power-normalized cepstral coefficients (PNCC) have been investigated to generate unique and robust feature vectors at different SNR values. Furthermore, to handle the large training data complexity, parameter optimization has been performed with sequence-discriminative training techniques: maximum mutual information (MMI), minimum phone error (MPE), boosted-MMI (bMMI), and state-level minimum Bayes risk (sMBR). It has been demonstrated by selection of an optimal value of parameters using lattice generation, and adjustments of learning rates. In proposed framework, four different systems have been tested by analyzing various feature extraction approaches (with or without speaker normalization through Vocal Tract Length Normalization (VTLN) approach in test set) and classification strategy on with or without artificial extension of train dataset. To compare each system performance, true matched (adult train and test—S1, child train and test—S2) and mismatched (adult train and child test—S3, adult + child train and child test—S4) systems on large adult and very small Punjabi clean speech corpus have been demonstrated. Consequently, gender-based in-domain data augmented is used to moderate acoustic and phonetic variations throughout adult and children’s speech under mismatched conditions. The experiment result shows that an effective framework developed on PNCC + VTLN front-end approach using TDNN-sMBR-based model through parameter optimization technique yields a relative improvement (RI) of 40.18%, 47.51%, and 49.87% in matched, mismatched and gender-based in-domain augmented system under typical clean and noisy conditions, respectively.

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Robust Feature Extraction of Speech Via Noise Reduction in Autocorrelation Domain

Cited by 12 publications

References 7 publications

Robust Feature Extraction Using Autocorrelation Domain for Noisy Speech Recognition

Robust Feature Extraction Using Autocorrelation Domain for Noisy Speech Recognition

An empirical research on the relation between the air transportation and economic growth

Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

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