Use of Grammatical Inference in Natural Speech Recognition

This paper presents the application of stochastic grammatical inference to speech recognition. In speech recognition, the acoustic signal process produces a set of words which are combinating to build sentences. Language models are then used to lead the speech recognition application to the most pertinent combination. Up to now, statistical language models are used. We suggest to use stochastic formal grammars instead of statistical models. Theses stochastic grammars will be build by machine learning algorithms. We will first show that unaided grammatical inference cannot be used for speech recognition. We will then make manifest that smoothing is necessary and show the gain that one can obtain by using a basic smoothing. We finally put up a smoothing technic dedicates to stochastic formal grammars. 2 THE QUALITY CRITERION 1 Introduction Our aim is to use stochastic grammatical inference for natural speech recognition. The main difference between validations of grammatical inference..

Using Symbol Clustering to Improve Probabilistic Automaton Inference

. In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERGIA algorithm combined with an independent clustering technique to the Air Travel Information System (ATIS) task. A 25 % reduction in perplexity was obtained. This result outperforms a trigram model under the same simple smoothing scheme. 1 Introduction Inference of deterministic finite automaton (DFA) from positive and negative data can be solved by the RPNI algorithm, proposed independently by Trakhtenbrot et al. [16] and by Oncina et al. [13]. This algorithm was used by Lang in his extensive experimental study of learning random deterministic automata from sparse samples [10]. An adapted version of this algorithm proved to be successful in the recent Abbadingo competition [9]. However th..