16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Unsupervised Word Discovery from Speech Using Automatic Segmentation into Syllable-Like Units

Okko Räsänen (1), Gabriel Doyle (2), Michael C. Frank (2)

(1) Aalto University, Finland
(2) Stanford University, USA

This paper presents a syllable-based approach to unsupervised pattern discovery from speech. By first segmenting speech into syllable-like units, the system is able to limit potential word onsets and offsets to a finite number of candidate locations. These syllable tokens are then described using a set of features and clustered into a finite number of syllable classes. Finally, recurring syllable sequences or individual classes are treated as word candidates. Feasibility of the approach is investigated on spontaneous American English and Tsonga language samples with promising results. We also present a new and simple, oscillator-based algorithm for efficient unsupervised syllabic segmentation.

Full Paper

Bibliographic reference.  Räsänen, Okko / Doyle, Gabriel / Frank, Michael C. (2015): "Unsupervised word discovery from speech using automatic segmentation into syllable-like units", In INTERSPEECH-2015, 3204-3208.