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Keywords
(15)
Continuous Speech Recognition
Em Algorithm
Environmental Variables
Noise Robustness
Noisy Data
Performance Improvement
Speech Recognition
Taylor Series
Expectation and Maximization
First Order
Hidden Markov Model
Maximum Likelihood
Parallel Model Combination
Speaker Independent
Vector Taylor Series
Related Publications
(10)
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Speech recognition in noisy environments using firstorder vector Taylor series
Speech recognition in noisy environments using firstorder vector Taylor series,10.1016/S01676393(97)000617,Speech Communication,Do Yeong Kim,Chong
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Speech recognition in noisy environments using firstorder vector Taylor series
(
Citations: 48
)
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Do Yeong Kim
,
Chong Kwan Un
,
Nam Soo Kim
Ž. In this paper, we generalize relations between clean and noisy speech signal using
vector Taylor series
VTS expansion Ž. for noiserobust speech recognition. We use it for both the
noisy data
compensation and
hidden Markov model
HMM parameter adaptation, and apply it for the cepstral domain directly, while Moreno used it to estimate the logspectral parameters. Also, we develop a detailed procedure to estimate
environmental variables
in the cepstral domain using the Ž. Ž.
expectation and maximization
EM algorithms based on the
maximum likelihood
ML sense. To evaluate the developed method, we conduct speakerindependent isolated word and
continuous speech recognition
experiments. White Gaussian and driving car noises added to clean speech at various SNR are used as disturbing sources. Using only noise statistics obtained from three frames of silence and noisy speech to be recognized, we achieve significant performance improvement. Ž. Especially, HMM parameter adaptation with VTS is more effective than the
parallel model combination
PMC based on the lognormal assumption. q 1998 Elsevier Science B.V. All rights reserved. Resume ´´
Journal:
Speech Communication
, vol. 24, no. 1, pp. 3949, 1998
DOI:
10.1016/S01676393(97)000617
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Citation Context
(34)
...In this case an auxiliary function is maximised using either secondorder schemes, [5, 2, 6], or EMbased optimisation approaches [
7
, 4]. The latter solutions are closely related to generalisations of the Factor Analysis (FA) framework...
...Eq. 4 has the form of a general FAstyle model for which EMbased update formulae can be applied to estimate both clean or noise parameters [8,
7
, 4, 3]. For example for noise parameter estimation, ˆ...
...Σz. Note the convolutional noise, h, is completely described by its mean, ˆ μ h . It can again be estimated by maximising Eq. 7, yielding a similar expression to that obtained in [
7
]...
F. Flego
,
et al.
Factor analysis based VTS and JUD noise estimation and compensation
...Wellknown methods included in this last subcategory are cepstral mean normalization (CMN) [10], histogram equalization (HEQ) [11], [12], vector Taylor series (VTS) expansion [13], [
14
], and more recently a noise suppression algorithm based on the minimum mean square error (MMSE) criterion [15]...
José A. González
,
et al.
Efficient MMSE Estimation and Uncertainty Processing for Multienvironm...
...Understandably, a simple linear approximation, namely the firstorder vector Taylor series (VTS) approximation, has been tried in the past (e.g., [
14
], [16], and [17])...
...In this appendix, we summarize how to derive, by extending the formulations in, e.g., [
14
] and [20], a procedure for the estimation of the parameters of explicit distortion model by maximizing the likelihood function defined on a given set of noisy observations in cepstral domain...
Jun Du
,
et al.
A Feature Compensation Approach Using HighOrder Vector Taylor Series ...
...Examples of modelbased methods are Vector Taylor Series for feature normalization (VTS) [
6
], Codeword Dependent Cepstral Normalization (CDCN) [7], and Spectral Subtraction (SS) [8]...
Luis Buera
,
et al.
Unsupervised DataDriven Feature Vector Normalization With Acoustic Mo...
...The first technique is based on the idea of “irrelevant variability normalization” (IVN) [17] and uses the VTS model adaptation in [
13
] as the basis for its approach...
...Also, IVN is based on the VTS approach in [
13
], while NAT is based on the approach in [15]...
...The goal of the traditional VTS model adaptation, e.g., [
13
]‐[15], is to adapt the parameters of the HMM trained using clean data to the environment conditions of a test utterance...
...IVN is based on the VTS adaptation algorithm proposed in [
13
], while NAT uses VTS adaptation as performed in [14] and [15]...
...In [
13
] (and thus in IVN), the log probability of the complete data used in the EM auxiliary function includes the observed noisy speech and hidden variables for clean speech, channel, noise and model component index (cf...
...In [13] (and thus in IVN), the log probability of the complete data used in the EM auxiliary function includes the observed noisy speech and hidden variables for clean speech, channel, noise and model component index (cf. Equation (12) in [
13
])...
...We note, however, that by using the auxiliary function in [
13
], IVNdoes havetheadvantage of closedform update equations for both the means and variances, whereas an iterative approach is required for the variances in NAT...
Ozlem Kalinli
,
et al.
Noise Adaptive Training for Robust Automatic Speech Recognition
References
(14)
Application Of Vts To Environment Compensation With Noise Statistics
(
Citations: 11
)
Nam Soo Kim
,
Do Yeong Kim
,
Byung Goo Kong
,
Sang Ryong Kim
Published in 1997.
Speech Recognition in Noisy Environments
(
Citations: 116
)
Pedro J. Moreno
Published in 1996.
A maximumlikelihood approach to stochastic matching for robust speech recognition
(
Citations: 239
)
Ananth Sankar
,
ChinHui Lee
Journal:
IEEE Transactions on Speech and Audio Processing  IEEE SAP
, vol. 4, no. 3, pp. 190202, 1996
Robustness in Automatic Speech Recognition
(
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J. C. Junqua
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Published in 1996.
Filterbankenergy estimation using mixture and Markov models for recognition of noisy speech
(
Citations: 13
)
Adoram Erell
,
Mitchel Weintraub
Journal:
IEEE Transactions on Speech and Audio Processing  IEEE SAP
, vol. 1, no. 1, pp. 6876, 1993
Sort by:
Citations
(48)
The integration of principal component analysis and cepstral mean subtraction in parallel model combination for robust speech recognition
(
Citations: 1
)
Hadi Veisi
,
Hossein Sameti
Journal:
Digital Signal Processing
, vol. 21, no. 1, pp. 3653, 2011
Factor analysis based VTS and JUD noise estimation and compensation
F. Flego
,
M. J. F. Gales
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 47924795, 2011
Mismatch modeling and compensation for robust speaker verification
Yun Lei
,
John H. L. Hansen
Journal:
Speech Communication
, vol. 53, no. 2, pp. 257268, 2011
Efficient MMSE Estimation and Uncertainty Processing for Multienvironment Robust Speech Recognition
José A. González
,
Antonio M. Peinado
,
Angel M. Gomez
,
José L. Carmona
Journal:
IEEE Transactions on Audio, Speech & Language Processing  TASLP
, vol. 19, no. 5, pp. 12061220, 2011
A Feature Compensation Approach Using HighOrder Vector Taylor Series Approximation of an Explicit Distortion Model for Noisy Speech Recognition
Jun Du
,
Qiang Huo
Journal:
IEEE Transactions on Audio, Speech & Language Processing  TASLP
, vol. 19, no. 8, pp. 22852293, 2011