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Semi-tied covariance matrices for hidden Markov models,”
- IEEE Trans. Speech and Audio Processing,
, 1999
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Person identification using multiple cues
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... Abstract-This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on vis ..."
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Cited by 217 (1 self)
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Abstract-This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid ranWmeasurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system. Index Tenns-Template matching, robust statistics, correlation, face recognition, speaker recognition, learning, classification. I.
Acoustical and Environmental Robustness in Automatic Speech Recognition
, 1990
"... This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in d ..."
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Cited by 214 (13 self)
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This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for speech input. Without such processing, mismatches between training and testing conditions produce an unacceptable degradation in recognition accuracy. Two kinds of
Feature Warping for Robust Speaker Verification
- ISCA ARCHIVE
, 2001
"... We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, nonlinear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by cond ..."
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Cited by 191 (10 self)
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We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, nonlinear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval. We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation. This technique is a suitable feature post-processing method that may be combined with other techniques to enhance speaker recognition robustness under adverse conditions.
Signal modeling techniques in speech recognition
- PROCEEDINGS OF THE IEEE
, 1993
"... We have seen three important trends develop in the last five years in speech recognition. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similariry transform techniques, often used to norm ..."
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Cited by 181 (5 self)
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We have seen three important trends develop in the last five years in speech recognition. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similariry transform techniques, often used to normalize and decor-relate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal’s spectrum can be estimated in a closed-loop manner. In this paper, we review the signal processing components of these algorithms. These al-gorithms are presented as part of a unified view of the signal parameterization problem in which there are three major tasks: measurement, transformation, and statistical modeling. This paper is by no means a comprehensive survey of all possible techniques of signal modeling in speech recognition. There are far too many algorithms in use today to make an exhaustive survey feasible (and cohesive). Instead, this paper is meant to serve as a tutorial on signal processing in state-of-the-art speech recognition systems and to review those techniques most commonly used. In keeping with this goal, a complete mathematical description of each algorithm has been included in the paper.
The Use of Context in Large Vocabulary Speech Recognition
, 1995
"... decide which contexts are similar and can share parameters. A key feature of this approach is that it allows the construction of models which are dependent upon contextual effects occurring across word boundaries. The use of cross word context dependent models presents problems for conventional dec ..."
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Cited by 157 (0 self)
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decide which contexts are similar and can share parameters. A key feature of this approach is that it allows the construction of models which are dependent upon contextual effects occurring across word boundaries. The use of cross word context dependent models presents problems for conventional decoders. The second part of the thesis therefore presents a new decoder design which is capable of using these models efficiently. The decoder is suitable for use with very large vocabularies and long span language models. It is also capable of generating a lattice of word hypotheses with little computational overhead. These lattices can be used to constrain further decoding, allowing efficient use of complex acoustic and language models. The effectiveness of these techniques has been assessed on a variety of large vocabulary continuous speech recognition tasks and results are presented which analyse performance in terms of computational complexity and recognition accuracy. The experiments dem
An overview of text-independent speaker recognition: from features to supervectors
, 2009
"... This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of ..."
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Cited by 156 (37 self)
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This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.
Speech Recognition with Dynamic Bayesian Networks
, 1998
"... Dynamic Bayesian networks (DBNs) are a useful tool for representing complex stochastic processes. Recent developments in inference and learning in DBNs allow their use in real-world applications. In this paper, we apply DBNs to the problem of speech recognition. The factored state representation ena ..."
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Cited by 130 (9 self)
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Dynamic Bayesian networks (DBNs) are a useful tool for representing complex stochastic processes. Recent developments in inference and learning in DBNs allow their use in real-world applications. In this paper, we apply DBNs to the problem of speech recognition. The factored state representation enabled by DBNs allows us to explicitly represent long-term articulatory and acoustic context in addition to the phonetic-state information maintained by hidden Markov models (HMMs). Furthermore, it enables us to model the short-term correlations among multiple observation streams within single time-frames. Given a DBN structure capable of representing these long- and short-term correlations, we applied the EM algorithm to learn models with up to 500,000 parameters. The use of structured DBN models decreased the error rate by 12 to 29% on a large-vocabulary isolated-word recognition task, compared to a discrete HMM; it also improved significantly on other published results for the same task. Th...
Speech Recognition in Noisy Environments
- Ph. D. Dissertation, ECE Department, CMU
, 1996
"... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.1. Thesis goals . . . . . . . . . . . . . . . . . . . . . ..."
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Cited by 125 (3 self)
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.1. Thesis goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.2. Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Chapter 2 The SPHINX-II Recognition System . . . . . . . . . . . . . . . . . . . . . . 17 2.1. An Overview of the SPHINX-II System . . . . . . . . . . . . . . . . . . 17 2.1.1. Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2. Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . 20 2.1.3. Recognition Unit . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.4. Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.1.5. Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2. Experimental Tasks and Corpora . ...
HMM adaptation using vector Taylor series for noisy speech recognition
- in Proceedings of ICSLP 2000
, 2000
"... In this paper we address the problem of robustness of speech recognition systems in noisy environments. The goal is to estimate the parameters of a HMM that is matched to a noisy environment, given a HMM trained with clean speech and knowledge of the acoustical environment. We propose a method based ..."
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Cited by 119 (15 self)
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In this paper we address the problem of robustness of speech recognition systems in noisy environments. The goal is to estimate the parameters of a HMM that is matched to a noisy environment, given a HMM trained with clean speech and knowledge of the acoustical environment. We propose a method based on truncated vector Taylor series that approximates the performance of a system trained with that corrupted speech. We also provide insight on the approximations used in the model of the environment and compare them with the lognormal approximation in PMC. 1.
