A flexible and low-complexity entropy-constrained vector quantizer (ECVQ) scheme based on Gaussian mixture models (GMMs), lattice quantization, and arithmetic coding is presented. The source is assumed to have a probability density function of a GMM. An input vector is first classified to one of the mixture components, and the Karhunen-Loeve transform of the selected mixture component is applied to the vector, followed by quantization using a lattice structured codebook. Finally, the scalar elements of the quantized vector are entropy coded sequentially using a specially designed arithmetic coder. The computational complexity of the proposed scheme is low, and independent of the coding rate in both the encoder and the decoder. Therefore, the proposed scheme serves as a lower complexity alternative to the GMM based ECVQ proposed by Gardner, Subramaniam and Rao. The performance of the proposed scheme is analyzed under a high-rate assumption, and quantified for a given GMM. The practical performance of the scheme was evaluated through simulations on both synthetic and speech line spectral frequency (LSF) vectors. For LSF quantization, the proposed scheme has a comparable performance to at rates relevant for speech coding (20-28 bits per vector) with lower computational complexity.

It is well known that usage of an appropriate representation of the speech signal improves the performance of speech coders, recognizers, and synthesizers. In this paper we present a representation of speech that has the efficiency, in terms of being compact, similar to that of parametric modeling, but additionally has the completeness property of signal expansions. The resulting canonical representation of speech is suited for a wide range of speech processing applications and we demonstrate this through experiments related to coding and prosodic modification.

In this paper, we present a scalable entropy-constrained vector quantizer based on Gaussian mixture models (GMMs), lattice quantization, and arithmetic coding. We assume that the source has a probability density function of a GMM. The scheme is based on a mixture component classifier, the Karhunen Loeve transform of the component, followed by a lattice quantization. The scalar elements of the quantized vector are entropy coded using a specially designed arithmetic coder. The proposed scheme has a computational complexity that is independent of rate, and quadratic with respect to vector dimension. The design is flexible and allows for adjusting the desired target rate on-the-fly. We evaluated the performance of the proposed scheme on speech-derived source vectors. It was demonstrated that the proposed scheme outperforms a fixed-rate GMM based vector quantizer, and performs closely to the theoretical optimum.

Mutual information is commonly used in speech processing in the context of statistical mapping. Examples are the optimization of speech or speaker recognition algorithms, the computation of performance bounds on such algorithms, and bandwidth extension of narrow-band speech signals. It is generally ignored that speech-signal derived data usually have an intrinsic dimensionality that is lower than the dimensionality of the observation vectors (the dimensionality of the embedding space). In this paper, we show that such reduced dimensionality can affect the accuracy of the mutual information estimate significantly. We introduce a new method that removes the effects of singular probability density functions. The method does not require prior knowledge of the intrinsic dimensionality of the data. It is shown that the method is appropriate for speech-derived data.

An accurate estimation of the pitch is essential for many speech processing applications, such as speech synthesis, speech coding, and speech enhancement. A widely used assumption in most common pitch estimation methods is that pitch is constant over a segment of short duration. This assumption does not apply in reality and leads to inaccurate pitch estimates. In this paper, we present a method for continuous pitch estimation that is able to track fast changes. In the presented framework, the pitch is modeled by a B-spline expansion and optimized in a multistage procedure for increased robustness. The performance of the continuous optimization procedure is compared to state-of-the-art pitch estimation methods and is evaluated both for artificial speech-like signals with known pitch, and for real speech signals. The results of the experiments show that our method leads to a higher accuracy of the estimate of the pitch than state-of-the-art methods

Estimation of the differential entropy from observations of a random variable is of great importance for a wide range of signal processing applications such as source coding, pattern recognition, hypothesis testing, and blind source separation. In this paper, we present a method for estimation of the Shannon differential entropy that accounts for embedded manifolds. The method is based on high-rate quantization theory and forms an extension of the classical nearest-neighbor entropy estimator. The estimator is consistent in the mean square sense and an upper bound on the rate of convergence of the estimator is given. Because of the close connection between compression and Shannon entropy, the proposed method has an advantage over methods estimating the Renyi entropy. Through experiments on uniformly distributed data on known manifolds and real-world speech data we show the accuracy and usefulness of our proposed method.

In this paper, we develop a multi-channel noise reduction algorithm based on blind source separation (BSS). In contrast to general BSS algorithms that attempt to recover all the signals, we explicitly estimate only the speech signal. By tracking the minimum of the spectral density of the microphone signals, noise-only segments are identified. The coefficients of the unmixing matrix that are necessary to separate the speech are identified from these segments through the optimization of an appropriate energy criterion. Since the proposed method explicitly estimates the speech signal from the noisy mixture, it does not suffer from the permutation problem that is typical to conventional BSS techniques. The method is applicable to both instantaneous and convolutive mixtures and achieves the separation in a single step, without the need for iterations. Experimental results show superior performance compared to a general BSS algorithm.

In this paper, we investigate the dependency between the spectral envelopes of speech in disjoint frequency bands, one covering the telephone bandwidth from 0.3 kHz to 3.4 kHz and one covering the frequencies from 3.7 kHz to 8 kHz. The spectral envelopes are jointly modeled with a Gaussian mixture model based on mel-frequency cepstral coefficients and the log-energy-ratio of the disjoint frequency bands. Using this model, we quantify the dependency between bands through their mutual information and the perceived entropy of the high frequency band. Our results indicate that the mutual information is only a small fraction of the perceived entropy of the high band. This suggests that speech bandwidth extension should not rely only on mutual information between narrow- and high-band spectra. Rather, such methods need to make use' of perceptual properties to ensure that the extended signal sounds pleasant.

In this paper, we consider the effect of a bandwidth extension of narrow-band speech signals (0.3-3.4 kHz) to 0.3-8 kHz on speaker verification.Using covariance matrix based verification systems together with detection error trade-off curves, we compare the performance between systems operating on narrowband, wide-band (0-8 kHz), and bandwidth-extended speech.The experiments were conducted using different short-time spectral parameterizations derived from microphone and ISDN speech databases.The studied bandwidth-extension algorithm did not introduce artifacts that affected the speaker verification task, and we achieved improvements between 1 and 10 percent (depending on the model order) over the verification system designed for narrow-band speech when mel-frequency cepstral coefficients for the short-time spectral parameterization were used.

We present a new way of treating the problem of extending a narrow-band signal to a wide-band signal. For many cases of bandwidth extension, the high-band energy is overestimated, leading to undesirable audible artifacts. To overcome these problems we introduce an asymmetric cost-function in the estimation process of the high-band that penalizes over-estimates more than under-estimates of the energy in the high-band. We show that the resulting attenuation of the estimated high-band energy depends on the broadness of the a-posteriori distribution of the energy given the extracted information about the narrow-band. Thus, the uncertainty about how to extend the signal at the high-band influences the level of extension. Results from a listening test show that the proposed algorithm produces less artifacts.

In this paper we investigate the mutual information in speech between the spectral envelope of the high frequency band and low frequency bands of various widths. Direct methods on the computation of the mutual information often result in an excessive amount of data required even for modest situations. We reduce the required amount of data by quantizing the low band leading to a lower bound expression on the mutual information. We indicate by simulation that this lower bound is in the same order of magnitude as the true mutual information. Simulations on speech show that we have no less than 0.1 bit of shared information between the slope of the high band and the low frequency band from 0-4 kHz. Performing the analogous simulation with the gain of the high band we obtained no less than 0.45 bit of mutual information.