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Speech Analysis
, 1998
"... Contents 1 Introduction 4 1.1 What is Speech Analysis? . . . . . . . . . . . . . . . . . . . . 4 1.1.1 So what is an acoustic vector? . . . . . . . . . . . . . . 4 1.2 Why Speech Analysis? . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The problems of speech analysis . . . . . . . . . . . . . . ..."
Abstract

Cited by 351 (0 self)
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Contents 1 Introduction 4 1.1 What is Speech Analysis? . . . . . . . . . . . . . . . . . . . . 4 1.1.1 So what is an acoustic vector? . . . . . . . . . . . . . . 4 1.2 Why Speech Analysis? . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The problems of speech analysis . . . . . . . . . . . . . . . . . 7 1.4 Standard references for this course . . . . . . . . . . . . . . . 7 2 Background 7 2.1 Sampling theory . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Sampling frequency . . . . . . . . . . . . . . . . . . . . 7 2.1.2 Sampling resolution . . . . . . . . . . . . . . . . . . . . 8 2.2 Linear filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Finite Impulse Response filters . . . . . . . . . . . . . 8 2.2.2 Infinite Impulse Response filters . . . . . . . . . . . . . 11 2.3 The source filter model of speech . . . . . . . . . . . . . . . . 12 3 Filter bank Analysis 12 3.1 Spectrograms . . . . . . . . .
Noise power spectral density estimation based on optimal smoothing and minimum statistics
 IEEE TRANS. SPEECH AND AUDIO PROCESSING
, 2001
"... We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a ..."
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Cited by 267 (7 self)
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We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal. Based on the optimally smoothed power spectral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types.
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 211 (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 desktop microphone (rather than a closetalking 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
Support vector machines for speech recognition
 Proceedings of the International Conference on Spoken Language Processing
, 1998
"... Statistical techniques based on hidden Markov Models (HMMs) with Gaussian emission densities have dominated signal processing and pattern recognition literature for the past 20 years. However, HMMs trained using maximum likelihood techniques suffer from an inability to learn discriminative informati ..."
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Cited by 114 (2 self)
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Statistical techniques based on hidden Markov Models (HMMs) with Gaussian emission densities have dominated signal processing and pattern recognition literature for the past 20 years. However, HMMs trained using maximum likelihood techniques suffer from an inability to learn discriminative information and are prone to overfitting and overparameterization. Recent work in machine learning has focused on models, such as the support vector machine (SVM), that automatically control generalization and parameterization as part of the overall optimization process. In this paper, we show that SVMs provide a significant improvement in performance on a static pattern classification task based on the Deterding vowel data. We also describe an application of SVMs to large vocabulary speech recognition, and demonstrate an improvement in error rate on a continuous alphadigit task (OGI Aphadigits) and a large vocabulary conversational speech task (Switchboard). Issues related to the development and optimization of an SVM/HMM hybrid system are discussed.
Spectral Subtraction Based on Minimum Statistics
 in Proc. Euro. Signal Processing Conf. (EUSIPCO
, 1994
"... Abstract. This contribution presents and analyses an algorithm for the enhancement of noisy speech signals by means of spectral subtraction. In contrast to the standard spectral subtraction algorithm the proposed method does not need a speech activity detector nor histograms to learn signal statisti ..."
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Cited by 102 (6 self)
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Abstract. This contribution presents and analyses an algorithm for the enhancement of noisy speech signals by means of spectral subtraction. In contrast to the standard spectral subtraction algorithm the proposed method does not need a speech activity detector nor histograms to learn signal statistics. The algorithm is capable to track non stationary noise signals and compares favorably with standard spectral subtraction methods in terms of performance and computational complexity. Our noise estimation method is based on the observation that a noise power estimate can be obtained using minimum values of a smoothed power estimate of the noisy speech signal. Thus, the use of minimum statistics eliminates the problem of speech activity detection. The proposed method is conceptually simple and well suited for real time implementations. In this paper we derive an unbiased noise power estimator based on minimum statistics and discuss its statistical properties and its performance in the context of spectral subtraction. 1.
Quantile based noise estimation for spectral subtraction and wiener filtering
 in Proc. IEEE Int. Conf. Acoust., Speech, and Sig. Proc. (ICASSP’00
, 2000
"... Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps: First ..."
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Cited by 54 (0 self)
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Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps: First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averaging. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener ltering. The database used in the experiments comprises 6034 utterances of German digits and digit strings by 770 speakers in 10 dierent cars. Without noise reduction, we obtain an error rate of 11.7%. Quantile based noise estimation and Wiener ltering reduce the error rate to 8.6%. Similar improvements are achieved in an experiment with arti cial, nonstationary noise. 1.
Efficient voice activity detection algorithms using longterm speech information
 Speech Communication
, 2004
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A generalized subspace approach for enhancing speech corrupted by colored noise
 IEEE Trans. Speech Audio Process
, 2003
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MultiChannel Speech Enhancement In A Car Environment Using Wiener Filtering And Spectral Subtraction
 Proc. ICASSP 97
, 1997
"... This paper presents a multichannelalgorithm for speech enhancement for handsfree telephone systems in cars. This new algorithm takes advantage of the special noise characteristics in fast driving cars. The incoherence of the noise allows to use adaptive Wiener filtering in the frequencies above a ..."
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Cited by 46 (2 self)
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This paper presents a multichannelalgorithm for speech enhancement for handsfree telephone systems in cars. This new algorithm takes advantage of the special noise characteristics in fast driving cars. The incoherence of the noise allows to use adaptive Wiener filtering in the frequencies above a theoretically determined frequency. Below this frequency a smoothed spectral subtraction (SSS) is used to get an improved noise suppression. The algorithm yields better results in noise reduction with significantly less distortions and artificial noise than spectral subtraction or Wiener filtering alone. 1. INTRODUCTION The handset equipment for telephones in cars is a restriction and a potential risk for the driver. Only handsfree devices can overcome this problem. Two different approaches for handsfree devices can be pursued. The first one uses only one microphone [1, 2], whereas the second one is a multichannel approach [3, 4]. The most often used singlesensor method is spectral s...
A competitive minimax approach to robust estimation in linear models
 Institute of Technology
"... Abstract—We consider the problem of estimating, in the presence of model uncertainties, a random vector x that is observed through a linear transformation H and corrupted by additive noise. We first assume that both the covariance matrix of x and the transformation H are not completely specified and ..."
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Cited by 46 (18 self)
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Abstract—We consider the problem of estimating, in the presence of model uncertainties, a random vector x that is observed through a linear transformation H and corrupted by additive noise. We first assume that both the covariance matrix of x and the transformation H are not completely specified and develop the linear estimator that minimizes the worstcase meansquared error (MSE) across all possible covariance matrices and transformations H in the region of uncertainty. Although the minimax approach has enjoyed widespread use in the design of robust methods, we show that its performance is often unsatisfactory. To improve the performance over the minimax MSE estimator, we develop a competitive minimax approach for the case where H is known but the covariance of x is subject to uncertainties and seek the linear estimator that minimizes the worstcase regret, namely, the worstcase difference between the MSE attainable using a linear estimator, ignorant of the signal covariance, and the optimal MSE attained using a linear estimator that knows the signal covariance. The linear minimax regret estimator is shown to be equal to a minimum MSE (MMSE) estimator corresponding to a certain choice of signal covariance that depends explicitly on the uncertainty region. We demonstrate, through examples, that the minimax regret approach can improve the performance over both the minimax MSE approach and a “plug in ” approach, in which the estimator is chosen to be equal to the MMSE estimator with an estimated covariance matrix replacing the true unknown covariance. We then show that although the optimal minimax regret estimator in the case in which the signal and noise are jointly Gaussian is nonlinear, we often do not lose much by restricting attention to linear estimators. Index Terms—Covariance uncertainty, linear estimation, minimax mean squared error, regret, robust estimation. I.