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We present probabilistic models for use in detecting and tracking topics in broadcast news stories. Our information retrieval (IR) models are formally explained. The topic detection and tracking (TDT) initiative is discussed. The application of probabilistic models to the topic detection and tracking tasks is developed, and enhancements are discussed. We discuss four variations of these models, and we report our preliminary test results from the current TDT corpus.

ABSTRACT Through the present paper, the evaluation results of aSpeaker Independent Robust-to-Noise Isolated WordRecognizer are presented. The system, which is in partthe results achieved by the project IVORY (ESPRITproject No. 20277) [6], is intended for working in highlynon-stationary environments. The system comprises twomain modules: the Noise Cancellator and the SpeechRecognizer itself. System robustness is achieved in thenoise cancellation module. This module incorporates anAdaptive Filter [7] [13], operating in the time domain,and a subsequent Spectral Subtraction step [2] operatingin the frequency domain with the enhanced signalprovided by the previous stage. Recognition results fordifferent noise cancellation configurations and forseveral Parameter Extraction Front-Ends, includingLPC [7], FFT Cepstrum [3] and PLP based methods [8]are presented.Keywords: robust speech recognition, non-stationaryenvironments, recognition results. 1. INTRODUCTION Environmental noise is one of the major problems to befaced by speech recognition systems working under realconditions, as most systems performance degradesubstantially with low S/N ratios [1]. This is particularlyimportant in non-stationary environments as in cars,plane cockpits, workshops with heavy machinery,crowded public places, etc. These scenarios arecharacterized by high noise levels (of about 95 dB withSNR´s about 0- 10 dB) produced by different sourcesand where noise contributions are especially harsh, asmany different noise sources may be activesimultaneously, varying dramatically with time. To facewith the treatment of noise, Robust Speech Recognitionhas become an objective in itself [4].Along the years, several independent techniques havebeen proposed. Most of them are based in two basicapproaches: Adaptive Time-Domain Filtering (ATDF)[14], and Frequency-Domain Spectral Subtraction(FDSS) [2]. Both methods have been used independentlywith different degree of success, depending on theproblem being solved. Generally, ATDF has been usedwith array microphones, ranging from two-microphoneto multiple-microphone systems. On the other hand,FDSS has been preferred for systems where a singlemicrophone is a requirement (e.g. Telephone speechrecognition). This technique has found its niche inapplications where noise is more or less a quasi-stationary and evenly distributed process. ATDF has theadvantage of being highly suitable to deal with non-stationary signals as speech and most kinds of noise,although at a higher cost (both in instrumentation and incomputational power). Although both methods can beintegrated in the same system, most of the times theyhave been used separately [5]. However, the co-operation of both procedures allows important gains inthe S/N ratio to be achieved, improving the overallrecognition results, as well.

We present a new method of speaker adapted training (SAT) that is more robust, faster, and results in lower error rate than the previous methods. The method, called inverse transform SAT (IT-SAT) is based on removing the differences between speakers before training, rather than modeling the differences during training. We develop several methods to avoid the problems associated with inverting the transformation. In one method, we interpolate the transformation matrix with an identity or diagonal transformation. We also apply constraints to the matrix to avoid estimation problems. Finally, we show that the resulting method is much faster, requires much less disk space, and results in higher accuracy than the original SAT method.

We describe our work on automatic transcription of radio and television news broadcasts. This problem is very challenging for large vocabulary speech recognition because of the frequent and unpredictable changes that occur in speaker, speaking style, topic, channel, and background conditions. Faced with such a problem, there is a strong tendency to try to carve the input into separable classes and deal with each one independently. In our early work on this problem, however, we are finding that the rewards for condition-specific techniques are disappointingly small. This is forcing us to look for general, robust, and adaptive algorithms for dealing with extremely variable data. We describe the BBN BYB-LOS recognition system configured to handle off-line transcription and we characterize the speech contained in the 1996 DARPA Hub-4 testbed. On the partitioned development test set, we achieved a 29.4% overall word error rate.

We present a general method for automatic reconstruction of accurate, concise, piecewise smooth surface models from scattered range data. The method can be used in a variety of applications such as reverse engineering—the automatic generation of CAD models from physical objects. Novel aspects of the method are its ability to model surfaces of arbitrary topological type and to recover sharp features such as creases and corners. The method has proven to be effective, as demonstrated by a number of examples using both simulated and real data.