This special issue is devoted to the synchronous approach to reactive and real-time programming. This introductory paper presents and discusses the application fields and the principles of synchronous programming. The major concern of the synchronous approach is to base synchronous programming languages on math-ematical models. This makes it possible to handle compilation, logical correctness proofs, and verifications of real-time programs in a formal way, leading to a clean and precise methodology for design and programming. 1. INTRODUCTION: REAL-TIME AND REACTIVE SYSTEMS It is commonly accepted to call real-time a program or system that receives external interrupts or reads sensors connected to the physical world and outputs commands to it. Real-time programming is an essential industrial activ-

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

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.

m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

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.

A process monitor for robotic assembly based on Hidden Markov Models (HMMs) is presented. The HMM process monitor is based on the dynamic force/torque signals arising from interaction between the workpiece and the environment. The HMMs represent a stochastic, knowledge-based system where the models are trained off-line with the Baum-Welch re-estimation algorithm. The assembly task is modeled as a discrete event dynamic system, where a discrete event is defined as a change in contact state between the workpiece and the environment. Our method 1) allows for dynamic motions of the workpiece, 2) accounts for sensor noise and friction and 3) exploits the fact that the amount of force information is large when there is a sudden change of discrete state in robotic assembly. After the HMMs have been trained, we use them on-line in a 2D experimental setup to recognise discrete events as they occur. Successful event recognition with an accuracy as high as 97% was achieved in 0.5-0.6 seconds with...

Most large vocabulary continuous speech recognition (LVCSR) systems in the past decade have used a context-dependent phone as the fundamental acoustic unit. In this paper, we present one of the first robust LVCSR systems that uses a syllable-level acoustic unit for LVCSR on telephone-bandwidth speech. This effort is motivated by the inherent limitations in phone-based approaches — namely the lack of an easy and efficient way for modeling long-term temporal dependencies. A syllable unit spans a longer time frame, typically three phones, thereby offering a more parsimonious framework for modeling pronunciation variation in spontaneous speech. We present encouraging results which show that a syllable-based system exceeds the performance of a comparable triphone system both in terms of word error rate (WER) and complexity. The WER of the best syllable system reported here is 49.1 % on a standard SWITCHBOARD evaluation, a small improvement over the triphone system. We also report results on a much smaller recognition task, OGI Alphadigits, which was used to validate some of the benefits syllables offer over triphones. The syllable-based system exceeds the performance of the triphone system by nearly 20%, an impressive accomplishment since the alphadigits application consists mostly of phone-level minimal pair distinctions.

In this paper, we describe the use of a powerful machine learning scheme, Support Vector Machines (SVM), within the framework of hidden Markov model (HMM) based speech recognition. The hybrid SVM/HMM system has been developed based on our public domain toolkit. The hybrid system has been evaluated on the OGI Alphadigits corpus and performs at 11.6% WER, as compared to 12.7% with a triphone mixture-Gaussian HMM system, while using only a fifth of the training data used by triphone system. Several important issues that arise out of the nature of SVM classifiers have been addressed. We are in the process of migrating this technology to large vocabulary recognition tasks like SWITCHBOARD. 1. INTRODUCTION Speech recogn i t i on can be v i ewed as a pa t t ern recognition problem where we desire each unique sound t o be d i s t i ngu i shab l e f r om a l l o t he r sounds . Traditionally statistical models, such as Gaussian mixture models, have been used to "represent" th...

ABSTRACT 2 Speaker-independent speech recognition technology has made significant progress from the days of isolated word recognition. Today, state-of-the-art systems are capable of performing large vocabulary continuous speech recognition (LVCSR) on audio streams derived from complex information sources such as broadcast news and two-way telephone dialogs. A significant contribution to this advancement in technology is the development of search techniques that find suboptimal but accurate solutions in problems involving large search spaces and extremely complex statistical models. Moreover, these search strategies are capable of dynamically integrating information from a number of diverse knowledge sources to determine the correct word hypothesis, and limit the scope of the search by using a hierarchical search strategy. We refer to this problem as the decoding or search problem. This paper describes the complexity associated with decoding using hierarchical representations for linguistic and acoustic knowledge sources. An extensible object-oriented decoder available in the public domain, that leverages current state-of-the-art technology is described to illustrate these concepts. This decoder supports efficient handling of acoustic models for cross-word contextdependent phones, multiple pronunciations of words using lexical trees, and rescoring of word graphs based on N-gram language models in a single pass. It employs a state-of-the-art Viterbistyle dynamic programming algorithm, and is equipped with several heuristic pruning criteria to minimize the consumption of computational resources while maintaining good accuracy.

A novel pattern classification technique and a new feature extraction method are described and tested for vowel classification. The pattern classification technique partitions an N-way classification task into N*(N-1)/2 two-way classification tasks. Each two-way classification task is performed using a neural network classifier that is trained to discriminate the two members of one pair of categories. Multiple two-way classification decisions are then combined to form an N-way decision. Some of the advantages of the new classification approach include the partitioning of the task allowing independent feature and classifier optimization for each pair of categories, lowered sensitivity of classification performance on network parameters, a reduction in the amount of training data required, and potential for superior performance relative to a single large network. The features described in this paper, closely related to the cepstral coefficients and delta cepstra commonly used in speech analysis, are developed using a unified mathematical framework which allows arbitrary nonlinear frequency, amplitude, and time scales to compactly represent the spectral/temporal characteristics of speech. This classification approach, combined with a feature-ranking algorithm which selected the 35 most discriminative spectral/temporal features for each vowel pair, resulted in 71.5 % accuracy for classification of 16 vowels extracted from the TIMIT database. These results, significantly higher than other published results for the same task, illustrate the potential for the methods presented in this paper. EDICS: SA1.6.3, SA1.6.1