A large vocabulary isolated word recognition system based on the hypothesize-and-test paradigm is described. The system has been, however, devised as a word hypothesizer for a continuous speech understanding system able to answer to queries put to a geographical database. Word preselection is achieved by segmenting and classifying the input signal in terms of broad phonetic classes. Due to low redundancy of this phonetic code for lexical access, to achieve high performance, a lattice of phonetic segments is generated, rather than a single sequence of hypotheses. It can be organized as a graph, and word hypothesization is obtained by matching this graph against the models of all vocabulary words. A word model is itself a phonetic representation made in terms of a graph accounting for deletion, substitution, and insertion errors. A modified Dynamic Programming (DP) matching procedure gives an efficient solution to this graph-to-graph matching problem. Hidden Markov Models (HMM's) of subword units are used as a more detailed knowledge in the verification step. The word candidates generated by the previous step are represented as sequences of diphone-like subword units, and the Viterbi algorithm is used for evaluating their likelihood. To reduce storage and computational costs, lexical knowledge is organized in a tree structure where the initial common subsequences of word descriptions are shared, and a beam-search strategy carries on the most promising paths only. The results show that a complexity reduction of about 73 percent can be achieved by using the two pass approach with respect to the direct approach, while the recognition accuracy remains comparable.

This paper presents a front-end consisting of an Artificial Neural Network (ANN) architecture trained with multilingual corpora. The idea is to train an ANN front-end able to integrate the acoustic variations included in databases collected for different languages, through different channels, or even for specific tasks. This ANN front-end produces discriminant features that can be used as observation vectors for language or task dependent recognizers.The approach has been evaluated on three difficult tasks: recognition of non-native speaker sentences, training of a new language with a limited amount of speech data, and training of a model for car environment using a clean microphone corpus of the target language and data collected in car environment in another language.

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