Forced alignment of child speech (Mahr et al., 2021)
journal contributionposted on 12.03.2021, 00:34 by Tristan J. Mahr, Visar Berisha, Kan Kawabata, Julie Liss, Katherine C. Hustad
Purpose: Acoustic measurement of speech sounds requires first segmenting the speech signal into relevant units (words, phones, etc.). Manual segmentation is cumbersome and time consuming. Forced-alignment algorithms automate this process by aligning a transcript and a speech sample. We compared the phoneme-level alignment performance of five available forced-alignment algorithms on a corpus of child speech. Our goal was to document aligner performance for child speech researchers.
Method: The child speech sample included 42 children between 3 and 6 years of age. The corpus was force-aligned using the Montreal Forced Aligner with and without speaker adaptive training, triphone alignment from the Kaldi speech recognition engine, the Prosodylab-Aligner, and the Penn Phonetics Lab Forced Aligner. The sample was also manually aligned to create gold-standard alignments. We evaluated alignment algorithms in terms of accuracy (whether the interval covers the midpoint of the manual alignment) and difference in phone-onset times between the automatic and manual intervals.
Results: The Montreal Forced Aligner with speaker adaptive training showed the highest accuracy and smallest timing differences. Vowels were consistently the most accurately aligned class of sounds across all the aligners, and alignment accuracy increased with age for fricative sounds across the aligners too.
Conclusion: The best-performing aligner fell just short of human-level reliability for forced alignment. Researchers can use forced alignment with child speech for certain classes of sounds (vowels, fricatives for older children), especially as part of a semi-automated workflow where alignments are later inspected for gross errors.
Supplemental Material S1. Analysis code.
Mahr, T. J., Berisha, V., Kawabata, K., Liss, J., & Hustad, K. C. (2021). Performance of forced-alignment algorithms on children's speech. Journal of Speech, Language, and Hearing Research. Advance online publication. https://doi.org/10.1044/2020_JSLHR-20-00268
Publisher Note: This article is part of the Special Issue: Select Papers From the 2020 Conference on Motor Speech.
This study was funded by Grants R01 DC015653 (awarded to Hustad) and R01 DC006859 (awarded to Liss/Berisha) from the National Institute on Deafness and Other Communication Disorders. Support was also provided by a core grant to the Waisman Center, U54 HD090256, from the National Institute of Child Health and Human Development.
Read the peer-reviewed publication
speechchildrenforced-alignmentalgorithmacousticmeasurespeech signalsegmentwordsphonesunitscomparetranscriptspeech sampleyoung childrenMontreal Forced AlignerspeakeradaptivetrainingKalda speech recognition engineProsodylab-AlignerPenn Phonetics Lab Forced Aligneralignmentaligneraccuracyautomaticmanualcomparisonvowelstimingsoundsfricativesemi-automatedworkflowproductionLinguistic Processes (incl. Speech Production and Comprehension)Laboratory Phonetics and Speech Science