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5 results on '"Panasonic Speech Technology Laboratory (PSTL)"'

1. Alpha-Jacobian environmental adaptation

Author

Cerisara, Christophe, Rigazio, Luca, Junqua, Jean-Claude, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Panasonic Speech Technology Laboratory (PSTL), and Panasonic

Subjects
noise robustness, reconnaissance automatique de la parole, automatic speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], jacobian adaptation, model compensation, adaptation jacobienne, compensation des modèles, robustesse au bruit
Abstract

Article dans revue scientifique avec comité de lecture. internationale.; International audience; The robustness of automatic speech recognition systems to noise is still a problem, especially for small footprint systems. This paper addresses the problem of noise robustness using model compensation methods. Such algorithms are already available, but their complexity is usually high. An often referenced method for achieving noise robustness is Parallel Model Combination (PMC). Several algorithms have been proposed to develop more computationally efficient methods than PMC. For example, Jacobian adaptation approximates PMC with a linear transformation function in the cepstral domain. However, the Jacobian approximation is valid only for test environments that are close to the training conditions whereas, in real test conditions, the mismatch between the test and training environments is usually large. In this paper, we propose two methods, respectively called static and dynamic alpha-Jacobian adaptation (or alpha-JAC), to compute new linear approximations of PMC for realistic test environments. We further extend both algorithms to compensate for additive and convolutional noise and we derive the corresponding non-linear algorithm that is approximated. All these algorithms are experimentally compared in important mismatch conditions. As compared to Jacobian adaptation, improvements are observed with both static and dynamic alpha-Jacobian adaptation.

Published
2004

2. Dynamic estimation of a noise over estimation factor for Jacobian-based adaptation

Author

Christophe Cerisara, Luca Rigazio, Jean-Claude Junqua, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Panasonic Speech Technology Laboratory (PSTL), and Panasonic

Subjects
Computer science, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Adaptation (eye), robustness, 02 engineering and technology, adaptation jacobienne, Machine learning, computer.software_genre, 01 natural sciences, Set (abstract data type), symbols.namesake, Robustness (computer science), 0103 physical sciences, jacobian adaptation, 0202 electrical engineering, electronic engineering, information engineering, Hidden Markov model, 010301 acoustics, business.industry, automatic speech recognition, 020206 networking & telecommunications, robustesse, Noise, reconnaissance automatique de la parole, Jacobian matrix and determinant, symbols, Artificial intelligence, business, Algorithm, computer
Abstract

Colloque avec actes et comité de lecture. internationale.; International audience; In this paper we propose an enhancement of the Jacobian adaptation by estimating automatically a noise over estimation factor which yields to a closer approximation of Parallel model combination (PMC) than the traditional Jacobian adaptation. Noise over estimation factors are estimated at run-time for a set of clustered Gaussians obtained on the training set. Experiments conducted on a French natural number database show that similar performance as PMC can be obtained at the expense of a slight increase in computational complexity as compared to Jacobian adaptation.

Published
2002
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3. Environmental adaptation based on first order approximation

Author

Robert C. Boman, Luca Rigazio, J.-C. Junqua, Christophe Cerisara, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), Panasonic Speech Technology Laboratory (PSTL), Panasonic, and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
reconnaissance de la parole, Noise pollution, Computer science, Speech recognition, Low-pass filter, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], speech recognition, Word error rate, 020206 networking & telecommunications, Environmental adaptation, 02 engineering and technology, robustness, adaptation, 01 natural sciences, robustesse, Noise, 13. Climate action, Robustness (computer science), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 010301 acoustics, Algorithm
Abstract

Colloque avec actes et comité de lecture. internationale.; International audience; In this paper, we propose an algorithm that compensates for both additive and convolutional noise. The goal of this method is to achieve an efficient environmental adaptation to realistic environments both in terms of computation time and memory. The algorithm described in this paper is an extension of an additive noise adaptation algorithm presented in [1]. Experimental results are given on a realistic database recorded in a car. This database is further filtered by a low pass filter to combine additive and channel noise. The proposed adaptation algorithm reduces the error rate by 75 % on this database, when compared to our baseline system without environmental adaptation.

Published
2001

4. Environment-adaptive algorithms for robust speech recognition

Author

Junqua, Jean-Claude, Cerisara, Christophe, Rigazio, Luca, Kryze, David, Panasonic Speech Technology Laboratory (PSTL), Panasonic, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2001

5. Transformation of Jacobian matrices for noisy speech recognition

Author

Cerisara, Christophe, Rigazio, Luca, Boman, Robert, Junqua, Jean-Claude, Analysis, perception and recognition of speech (PAROLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), Panasonic Speech Technology Laboratory (PSTL), Panasonic, and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
reconnaissance automatique de la parole, automatic speech recognition, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], adaptation
Abstract

Colloque avec actes et comité de lecture. internationale.; International audience; Robustness of automatic speech recognition systems to noise is still an issue, especially for small footprint systems. This paper addresses the problem of noise robustness using model compensation. Jacobian adaptation is an example of model compensation, which is also computationally efficient. However, its performance drops when the mismatch between training and testing environments increases. Our new adaptation algorithm is based on finding the best linear transformation for compensating the mismatch between training and testing conditions. In this paper, it is shown that, using a dimensionality reduction technique, time and memory requirements of the algorithm can be further decreased compared to the classical Jacobian adaptation. Furthermore, experimental results for an English digit recognition task, recorded in a car at 60 mph, show that the error rate decreases by 66 %, compared to the classical Jacobian adaptation.

Published
2000

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