11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Fully Unsupervised Word Learning from Continuous Speech Using Transitional Probabilities of Atomic Acoustic Events

Okko Johannes Räsänen

Aalto University, Finland

This work presents a learning algorithm based on transitional probabilities of atomic acoustic events. The algorithm learns models for word-like units in speech without any supervision, and without a priori knowledge of phonemic or linguistic units. The learned models can be used to segment novel utterances into word-like units, supporting the theory that transitional probabilities of acoustic events could work as a bootstrapping mechanism of language learning. The performance of the algorithm is evaluated using a corpus of Finnish infant-directed speech.

Full Paper

Bibliographic reference.  Räsänen, Okko Johannes (2010): "Fully unsupervised word learning from continuous speech using transitional probabilities of atomic acoustic events", In INTERSPEECH-2010, 2922-2925.