A Tunable Forced Alignment System Based on Deep Learning: Applications to Child Speech
Year: 2025
Type: article
Abstract: 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. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28593971.
Institutions: Arizona State University, University of Wisconsin–Madison
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409009558