Improving automatic forced alignment for dysarthric speech transcription
Yu Ting Yeung, Ka Ho Wong, Helen M. L. Meng · 2015
Dysarthria is a motor speech disorder due to neurologic deficits. The impaired movement of muscles for speech production leads to disordered speech where utterances have prolonged pause in-tervals, slow speaking rates, poor articulation of phonemes, syl-lable deletions, etc. These present challenges towards the use of speech technologies for automatic processing of dysarthric speech data. In order to address these challenges, this work be-gins by addressing the performance degradation faced in forced alignment. We perform initial alignments to locate long pauses in dysarthric speech and make use of the pause intervals as an-chor points. We apply speech recognition for word lattice out-puts for recovering the time-stamps of the words in disordered or incomplete pronunciations. By verifying the initial align-ments with word lattices, we obtain the reliably aligned seg-ments. These segments provide constraints for new alignment grammars, that can improve alignment and transcription quality. We have applied the proposed strategy to the TORGO corpus and obtained improved alignments for most dysarthric speech data, while maintaining good alignments for non-dysarthric speech data. Index Terms: automatic forced alignment, speech recognition, dysarthric speech, word lattices