A Tunable Forced Alignment System Based on Deep Learning: Applications to Child Speech.
Purpose: Phonetic forced alignment has a multitude of applications in automated analysis of speech, particularly in studying nonstandard speech such as children's speech. Manual alignment is tedious but serves as the gold standard for clinical-grade alignment. Current tools do not support direct tra...
| Published in: | Journal of Speech, Language & Hearing Research Vol. 68; pp. 3583 - 3602 |
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| Main Authors: | , , , |
| Format: | Article |
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American Speech-Language-Hearing Association
2025 Supplement
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187102507&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187102507 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: 2025 Supplement vid: 68 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 187102507 10.1044/2024_JSLHR-24-00347 ppf: 3583 ppct: 19 formats: fmt: @attributes: type: P size: 8.4MB tig: atl: A Tunable Forced Alignment System Based on Deep Learning: Applications to Child Speech. aug: au: Kadambi, Prad Mahr, Tristan J. Hustad, Katherine C. Berisha, Visar affil: School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe College of Health Solutions, Arizona State University, Tempe Waisman Center, University of Wisconsin--Madison Department of Communication Sciences and Disorders, University of Wisconsin--Madison su: Child development Phonetics Language acquisition Children Physiological adaptation Research funding Descriptive statistics Verbal behavior testing Audiometry Intelligibility of speech Physiological aspects of speech Deep learning Speech evaluation Speech audiometry Automation Comparative studies Articulation (Speech) sug: subj: Child development Phonetics Language acquisition Children Physiological adaptation Research funding Descriptive statistics Verbal behavior testing Audiometry Intelligibility of speech Physiological aspects of speech Deep learning Speech evaluation Speech audiometry Automation Comparative studies Articulation (Speech) ab: Purpose: Phonetic forced alignment has a multitude of applications in automated analysis of speech, particularly in studying nonstandard speech such as children's speech. Manual alignment is tedious but serves as the gold standard for clinical-grade alignment. Current tools do not support direct training on manual alignments. Thus, a trainable speaker adaptive phonetic forced alignment system, Wav2TextGrid, was developed for children's speech. The source code for the method is publicly available along with a graphical user interface at https://github.com/pkadambi/Wav2TextGrid. Method: We propose a trainable, speaker-adaptive, neural forced aligner developed using a corpus of 42 neurotypical children from 3 to 6 years of age. Evaluation on both child speech and on the TIMIT corpus was performed to demonstrate aligner performance across age and dialectal variations. Results: The trainable alignment tool markedly improved accuracy over baseline for several alignment quality metrics, for all phoneme categories. Accuracy for plosives and affricates in children's speech improved more than 40% over baseline. Performance matched existing methods using approximately 13 min of labeled data, while approximately 45--60 min of labeled alignments yielded significant improvement. Conclusion: The Wav2TextGrid tool allows alternate alignment workflows where the forced alignments, via training, are directly tailored to match clinical-grade, manually provided alignments. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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