Automatic discovery of topics and acoustic morphemes from speech
Résumé
This work deals with automatic lexical acquisition and topic discovery from a speech stream. The proposed algorithm builds a lexicon enriched with topic information in three steps: transcription of an audio stream into phone sequences with a speaker- and task-independent phone recogniser, automatic lexical acquisition based on approximate string matching, and hierarchical topic clustering of the lexical entries based on a knowledge-poor co-occurrence approach. The resulting semantic lexicon is then used to automatically cluster the incoming speech stream into topics. The main advantages of this algorithm are its very low computational requirements and its independence to pre-defined linguistic resources, which makes it easy to port to new languages and to adapt to new tasks. It is evaluated both qualitatively and quantitatively on two corpora and on two tasks related to topic clustering. The results of these evaluations are encouraging and outline future directions of research for the proposed algorithm, such as building automatic orthographic labels of the lexical items.