The Mason-Alberta Phonetic Segmenter: a forced alignment system based on deep neural networks and interpolation.

ABSTRACT: Given an orthographic transcription, forced alignment systems automatically determine boundaries between segments in speech, facilitating the use of large corpora. In the present paper, we introduce a neural network-based forced alignment system, the Mason-Alberta Phonetic Segmenter (MAPS). MAPS serves as a testbed for two possible improvements we pursue for forced alignment systems. The first is treating the acoustic model as a tagger, rather than a classifier, motivated by the common understanding that segments are not truly discrete and often overlap. The second is an interpolation technique to allow more precise boundaries than the typical 10 ms limit in modern systems. During testing, all system configurations we trained significantly outperformed the state-of-the-art Montreal Forced

SUBMITTER: Kelley MC 

PROVIDER: S-EPMC11449383 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature