The modified group delay function and its application to phoneme recognition

Murthy, Hema A.; Gadde, Venkata Ramana Rao

doi:10.1109/icassp.2003.1198718

Cited by 92 publications

(63 citation statements)

References 4 publications

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“…Specifically, it can only be used for MFCC or log filterbank energies (FBE) and can not be directly applied to representations which -instead of log compression -apply a power transformation to the FBEs. Examples of such features are generalised-MFCC [12], PLP [3], PNCC [5] and phase-based features [4,[13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Use of Generalised Nonlinearity in Vector Taylor Series Noise Compensation for Robust Speech Recognition

2016

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Designing good normalisation to counter the effect of environmental distortions is one of the major challenges for automatic speech recognition (ASR). The Vector Taylor series (VTS) method is a powerful and mathematically well principled technique that can be applied to both the feature and model domains to compensate for both additive and convolutional noises. One of the limitations of this approach, however, is that it is tied to MFCC (and log-filterbank) features and does not extend to other representations such as PLP, PNCC and phase-based front-ends that use power transformation rather than log compression. This paper aims at broadening the scope of the VTS method by deriving a new formulation that assumes a power transformation is used as the non-linearity during feature extraction. It is shown that the conventional VTS, in the log domain, is a special case of the new extended framework. In addition, the new formulation introduces one more degree of freedom which makes it possible to tune the algorithm to better fit the data to the statistical requirements of the ASR back-end. Compared with MFCC and conventional VTS, the proposed approach provides upto 12.2% and 2.0% absolute performance improvements on average, in Aurora-4 tasks, respectively.

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Section: Introductionmentioning

confidence: 99%

Use of Generalised Nonlinearity in Vector Taylor Series Noise Compensation for Robust Speech Recognition

2016

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“…In this method, the zeros near the unit circle in Equation 3 are suppressed by smoothing the magnitude spectrum X(w) [Murthy and Gadde (2003); Hegde et al (2007b)]. The resulting group delay function is well behaved.…”

Section: Modified Group Delay Function (Modgd)mentioning

confidence: 99%

Robustness of group delay representations for noisy speech signals

Parthasarathi

Rajan

Murthy

2011

Int J Speech Technol

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This paper demonstrates the robustness of group delay based features to additive noise. First, we analytically show the robustness of group delay based representations. The analysis makes use of the fact that, for minimum-phase signals, the group delay function can be represented in terms of the cepstral coefficients of the log-magnitude spectrum. Such a representation results in the speech spectrum dominating over the noise spectrum, both at low and high SNRs. Further, we experimentally demonstrate the robustness of the representation on a voice activity detection (VAD) task, comparing a group delay based VAD algorithm with standard VAD methods as well as a magnitude-spectrum based method.

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“…)| using low-order cepstral lifter l w . α, γ and l w are adjusted according to the particular requirements [7]. The GDMD feature is computed in the same way as the MD one, but instead the FFT magnitude spectrum the MGDS is used [12].…”

Section: -4 2014mentioning

confidence: 99%

Mean-Delta Features for Telephone Speech Endpoint Detection

Ouzounov¹

2014

Information Technologies and Control

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The modified group delay function and its application to phoneme recognition

Cited by 92 publications

References 4 publications

Use of Generalised Nonlinearity in Vector Taylor Series Noise Compensation for Robust Speech Recognition

Use of Generalised Nonlinearity in Vector Taylor Series Noise Compensation for Robust Speech Recognition

Robustness of group delay representations for noisy speech signals

Mean-Delta Features for Telephone Speech Endpoint Detection

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