Automatic training of stochastic finite-state language models for speech understanding

The representation of language models through stochastic finite state networks offers several attractive features for speech recognition and understanding, including the ease of integration with the algorithms used for acoustic-phonetic decoding. However, the application to real-world problems is difficult because the network sizes grow very large, and their training requires grammatical inference methods. An approach is described that keeps the network size small by avoiding detailed modeling of linguistic constructs not essential for understanding the meaning of sentences. An automatic method for training such networks starting from a corpus of semantically annotated sentences is described. The training procedure learns both the network structure and the rules for generating the semantic representation of sentences. Testing of a 787-word task achieved 92% correct sentence understanding with written input and 72% with continuous speech, speaker-independent, telephone-bandwidth spoken input