Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and nonGaussian statistics often encountered in practice. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal models. Efficient Expectation Maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classification, and detectio...

We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forwardfiltering backward-smoothing procedure which can be viewed as the non-linear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean-squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterised in terms of timevarying partial correlation coe#cients, comparing the results of our algorithm with those from a simple smoother based upon the filtered trajectories.

Speech segregation is an important task of auditory scene analysis (ASA), in which the speech of a certain speaker is separated from other interfering signals. Wang and Brown proposed a multistage neural model for speech segregation, the core of which is a two-layer oscillator network. In this paper, we extend their model by adding further processes based on psychoacoustic evidence to improve the performance. These processes include pitch tracking and grouping based on amplitude modulation (AM). Our model is systematically evaluated and compared with the Wang-Brown model, and it yields significantly better performance. 1.

We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set.

Graphical models provide a promising paradigm to study both existing and novel techniques for automatic speech recognition. This paper first provides a brief overview of graphical models and their uses as statistical models. It is then shown that the statistical assumptions behind many pattern recognition techniques commonly used as part of a speech recognition system can be described by a graph – this includes Gaussian distributions, mixture models, decision trees, factor analysis, principle component analysis, linear discriminant analysis, and hidden Markov models. Moreover, this paper shows that many advanced models for speech recognition and language processing can also be simply described by a graph, including many at the acoustic-, pronunciation-, and language-modeling levels. A number of speech recognition techniques born directly out of the graphical-models paradigm are also surveyed. Additionally, this paper includes a novel graphical analysis regarding why derivative (or delta) features improve hidden Markov model-based speech recognition by improving structural discriminability. It also includes an example where a graph can be used to represent language model smoothing constraints. As will be seen, the space of models describable by a graph is quite large. A thorough exploration of this space should yield techniques that ultimately will supersede the hidden Markov model.

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 over-parameterization. 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.

We have made significant progress in automatic speech recognition (ASR) for well-defined applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, ASR performance has yet to reach the level required for speech to become a truly pervasive user interface. Indeed, even in “clean ” acoustic environments, and for a variety of tasks, state of the art ASR system

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.

We present a new error concealment technique for audio transmission over heterogeneous packet switched networks based on time-scale modification of correctly received packets. An appropriate time-scale modification algorithm, WSOLA ("Waveform Similarity OverlapAdd "), is used and its parameters are optimized for scaling short audio segments. Particular attention is paid to the additional delay introduced by the new technique. For subjective listening tests, packet loss is simulated at error rates of 20% and 33% and the new technique is compared to previous proposals by category and component judgment of speech quality. The test results show that typical disturbance components of other techniques can be avoided and overall sound quality is higher. 1 Introduction Today's packet switched networks are more and more used not only for bulk data transfer, but for audio and video transmission. An example is the MBone overlay network in the Internet. However, packet switched networks typically...

This paper addresses the limitations of current approaches to distant-talker speech acquisition and advocates the development of techniques which explicitly incorporate the nature of the speech signal (e.g. statistical non-stationarity, method of production, pitch, voicing, formant structure, and source radiator model) into a multi-channel context. The goal is to combine the advantages of spatial filtering achieved through beamforming with knowledge of the desired time-series attributes. The potential utility of such an approach is demonstrated through the application of a multi-channel version of the Dual Excitation speech model. 1. INTRODUCTION For close-talker environments, single channel speech enhancement systems have been well-studied and proven effective at improving the perceived quality of speech degraded by low to moderate levels of background noise. Examples of these approaches include Spectral Subtraction and its many variations, Wiener Filtering, Adaptive Noise Cancellati...