A morphological model, applicable to inflected languages, which combines the robustness of the tripos model with the prediction power of the lemma is proposed. A semantic component acts at the lemma level, without taking into account the different inflections of a lemma, thus making its trainable even for 200000 words. The training corpus for the lemma model (consisting of 38 million words) is labeled in terms of lemma and part of speech, using a semiautomatic process. The results obtained with this new model are reported. The model shows another way to put knowledge in the pure probabilistic framework of hidden Markov models.>

The authors experiment with a combination of two methods previously proposed to improve the performance of their speech recognition system. One method is based on the definition of an improved system of phonetic units, which takes into account the most important coarticulation effects. This system has been defined using knowledge about coarticulation, and by studying the errors of a standard phonetic system. The second method is based on the use of maximum mutual information (MMI) as a criterion in the training phase of the speech recognition system. MMI is designed to maximize the probability of the correct text versus the other possible texts, and it is expected to provide better discrimination of the correct text than the standard maximum-likelihood criterion. These methods have been tested independently on phonetic recognition, and each of them improved the recognition accuracy of the system. Results of recognition experiments that combine the two methods are presented and discussed. They show that this combination improves the average recognition rate, both for phonetic and word recognition.>

One approach to large vocabulary speech recognition, is to build phonetic Markov models, and to concatenate them to obtain word models. In previous work, we already designed a recognizer based on 40 phonetic Markov machines, which accepts a 10,000 words vocabulary ([3]), and recently 200,000 words vocabulary ([5]). Since there is one machine per phoneme, these models obviously do not account for coarticulatory effects, which may lead to recognition errors. In this paper, we improve the phonetic models by using general principles about coarticulation effects on automatic phoneme recognition. We show that both the analysis of the errors made by the recognizer, and linguistic facts about phonetic context influence, suggest a method for choosing context dependent models. This method allows to limit the growing of the number of phonems, and still account for the most important coarticulation effects. We present our experiments with a system applying these principles to a set of models for French. With this new system including context-dependant machines, the phoneme recognition rate goes from 82.2% to 85.3%, and the error rate on words with a 10,000 word dictionary, is decreased from 11.2 to 9.8%.

An approach for supporting large vocabulary in speech recognition is to use broad phonetic classes to reduce the search to a subset of the dictionary. In this paper, we investigate the problem of defining an optimal classification for a given speech decoder, so that these broad phonetic classes are recognized as accurately as possible from the speech signal. More precisely, given Hidden Markov Models of phonemes, we define a similarity measure of the phonetic machines, and use a standard classification algorithm to find the optimal classification. Three measures are proposed, and compared with manual classifications.

This paper relates different kinds of language modeling methods that can be applied to the linguistic decoding part of a speech recognition system with a very large vocabulary. These models are studied experimentally on a pseudophonetic input arising from French stenotypy. We propose a model which combines the advantages of a statistical modeling with information theoretic tools, and those of a grammatical approach.

In this paper, we study the combination of an information theoretic tool (Markov modeling of natural language [3]) with probabilistic grammatical analysis. Continuous Speech Recognition for natural language raises a lot of difficulties, both for the acoustic processing and the linguistic decoding. Our work specifically concerns the linguistic decoding techniques for a very large (140,000 entries) French dictionary, and a oral open discourse. So the task is to transcribe a continuous string of pseudo-phonemes into written text. This string would be ideally the output of a perfect acoustic processor. We present a grammar designed for automatic transcription and compute probabilities for the rules. We compare its results with those obtained earlier with Markov modeling. We show that it is possible to combine the two approaches and get better results than each model separately.

In this paper, we study the complexity of the recognition process for a large vocabulary, with a specific application using a large French dictionary. In the first part, we review the lexical and grammatical differences between French and English that affect recognition complexity. Then we compute various measurements on a large pseudo-phonetic French dictionary. In the second part, we report experiments on a large real text. To measure recognition complexity, we compute the average branching factor arising in all possible spellings of the phonetic transcription of the text, according to our dictionary. We show that a Markov modeling of French allows us to reduce significantly the branching factor by discarding improbable choices.