title: Using automatic alignment to analyze endangered language data: testing the viability of untrained alignment.
abstract: While efforts to document endangered languages have steadily increased, the phonetic analysis of endangered language data remains a challenge. The transcription of large documentation corpora is, by itself, a tremendous feat. Yet, the process of segmentation remains a bottleneck for research with data of this kind. This paper examines whether a speech processing tool, forced alignment, can facilitate the segmentation task for small data sets, even when the target language differs from the training language. The authors also examined whether a phone set with contextualization outperforms a more general one. The accuracy of two forced aligners trained on English (hmalign and p2fa) was assessed using corpus data from Yoloxóchitl Mixtec. Overall, agreement performance was relatively good, with accuracy at 70.9% within 30 ms for hmalign and 65.7% within 30 ms for p2fa. Segmental and tonal categories influenced accuracy as well. For instance, additional stop allophones in hmalign's phone set aided alignment accuracy. Agreement differences between aligners also corresponded closely with the types of data on which the aligners were trained. Overall, using existing alignment systems was found to have potential for making phonetic analysis of small corpora more efficient, with more allophonic phone sets providing better agreement than general ones.
authors: Haskins Laboratories, 300 George Street, New Haven, Connecticut 06511, USA. dicanio@haskins.yale.edu
authors: Christian
authors: C
authors: DiCanio
authors: Hosung
authors: H
authors: Nam
authors: Douglas H
authors: DH
authors: Whalen
authors: H Timothy
authors: HT
authors: Bunnell
authors: Jonathan D
authors: JD
authors: Amith
authors: Rey Castillo
authors: RC
authors: García
mesh_terms: D000162:Acoustics; D005240:Feasibility Studies; D006801:Humans; D010363:Pattern Recognition, Automated; D010700:Phonetics; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012985:Software Design; D013018:Sound Spectrography; D013061:Speech Acoustics; D013068:Speech Production Measurement; D014833:Voice Quality
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