Articles

In this paper we demonstrate that speech recognition can be effectively applied to information retrieval (IR) applications. Our system exploits the fact that the intended words of a spo- ken query tend to co-occur in text documents in close proxim- ity whereas word combinations that are the result of recogni- …...

A new mapping algorithm for speech recognition relates the fea- tures of simultaneous recordings of clean and noisy speech. The model is a piecewise nonfinear transformation appfied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters whose outputs are combined using a conditional Gaussian …...

This paper describes a series of ceps~al-based compensation pro- cedures that render the SPHINX-II system more robust with respect to acoustical environment. The first algorithm, phone- dependent cepstral compensation, is similar in concept to the pre- viously-described MFCDCN method, except that cepstral com- pensation vectors are selected according to the …...

BBN Systems and Technologies Cambridge MA 02138 tNortheastem University Boston MA 02115 In this paper, we present several approaches designed to increase the robustness of BYBLOS, the BBN continuous speech recogni- tion system. We address the problem of increased degradation in • performance when there is mismatch in the characteristics …...

A major axis of research at LIMSI is directed at multilingual, speaker-independent, large vocabulary speech dictation. In this pa- per the LIMSI recognizer which was evaluated in the ARPA NOV93 CSR test is described, and experimental results on the WSJ and BREF corpora under closely matched conditions are reported. For …...

Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that optimizes the degree of mixture tying for a given amount of training data and computational resources. Experimental results on the Wall Street Journal (WSJ) …...

The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance be- tween model complexity and available training data. For large vocabulary systems requiring cross-word context de- pendent modelling, this is particularly acute since many mmh contexts will never occur in the training data. …...

This paper explores use of synergistically-integrated systems of microphone arrays and neural networks for robust speech recognition in variable acoustic environments, where the user must not be encumbered by microphone equipment. Existing speech recognizers work best for "high-quality close-talking speech." Performance of these recognizers is typically de- graded by environmental …...

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In this paper, we describe a reversible letter-to-sound/sound- to-letter generation system based on an approach which com- bines a rule-based formalism with data-driven techniques. We adopt a probabilistic parsing strategy to provide a hierarchical lexical analysis of a word, including information such as mor- phology, stress, syllabification, phonemics and graphemics. …...

The processing of Japanese text is complicated by the fact that there are no word delimiters. To segment Japanese text, systems typically use knowledge-based methods and large lexicons. This paper presents a novel approach to Japanese word segmentation which avoids the need for Japanese word lexicons and explicit rule bases. …...

This paper introduces a class of statistical mechanisms, called hidden understanding models, for natural language processing. Much of the framework for hidden understanding models derives from statistical models used in speech recognition, especially the use of hidden Markov models. These techniques are applied to the central problem of determining meaning …...

We describe a series of three experiments in which supervised learning techniques were used to acquire three different types of grammars for English news stories. The acquired grammar types were: 1) context-free, 2) context-dependent, and 3) probabilistic context-free. Training data were derived from University of Pennsylvania Treebank parses of 50 …...

We present the concepts of weighted language, ~ansduction and au- tomaton from algebraic automata theory as a general framework for describing and implementing decoding cascades in speech and lan- guage processing. This generality allows us to represent uniformly such information sources as pronunciation dictionaries, language models artd lattices, and to …...

Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typi- cally in tens of thousands of lexical and contextual probabili- ties. In [Brill 92], a trainable rule-based tagger was described that obtained performance comparable to …...

This paper presents and evaluates models created according to a schema that provides a description of the joint distribu- tion of the values of sense tags and contextual features that is potentially applicable to a wide range of content words. The models are evaluated through a series of experiments, the …...

This paper proposes benchmarks for systems of automatic sense identification. A textual corpus in which open-class words had been tagged both syntactically and semantically was used to explore three statistical strategies for sense identification: a guess- ing heuristic, a most-frequent heuristic, and a co-occurrence heuris- tic. When no information about …...

We present an integrated approach to speech and natural language processing which uses a single parser to create training for a statistical speech recognition component and for interpreting recognized text. On the speech recognition side, our innovation is the use of a statistical model combining N-gram and context-free grammars. On …...

This research characterizes the spontaneous spoken disfluen- cies typical of human-computer interaction, and presents a predictive model accounting for their occurrence. Data were collected during three empirical studies in which people spoke or wrote to a highly interactive simulated system. The stud- ies involved within-subject factorial designs in which input …...

A simple and general method is described that can combine different knowledge sources to reorder N-best lists of hypothe- ses produced by a speech recognizer. The method is automat- ically trainable, acquiring information from both positive and negative examples. In experiments, the method was tested on a 1000-utterance sample of …...