A Tunable Forced Alignment System Based on Deep Learning: Applications to Child Speech.

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 cor

SUBMITTER: Kadambi P 

PROVIDER: S-EPMC12337111 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature