Sentence boundary detection without speech recognition : a case of an underresourced language

Abstract EN

Sentence boundary detection (SBD), also known as sentence segmentation decides where a sentence begins and ends.

Previous method of SBD is either done by linguistic approach or acoustic approach; or combination of both approaches.

Even though linguistic approach generally performed better than acoustic approach, it requires the need of a speech recognition component.

This is a constraint for Under Resource Languages such as the Malay language.

This paper describes the SBD for spontaneous Malay language spoken audio.

Experiments are conducted on a forty-two minutes question-answer (Q/A) Malaysia parliamentary session comprising 12 adult male speakers and 4 female speakers.

The speech datasets are first classified as speech/non-speech segments and only the non-speech segments are further tested as candidates of sentence boundaries.

Seven prosodic features, rate-of-speech and volume are then extracted from the boundary candidates for classification.

Our proposed SBD method using supervised Adaboost classifier managed a promising100% accuracy rate with 19.44% error rate.

For future work, we intend to reduce the error rate by implementing end-point detection on the boundary candidates.