Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling

• URL: http://arxiv.org/abs/2306.00996v1
• Date: Tue, 30 May 2023 09:57:36 GMT
• Title: Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling
• Authors: Theodoros Kouzelis, Georgios Paraskevopoulos, Athanasios Katsamanis, Vassilis Katsouros
• Abstract summary: We propose a simple and effective modification of alignment graph construction using weighted Finite State Transducers. The proposed weakly-supervised approach alleviates the need for verbatim transcription of speech disfluencies for forced alignment. Our evaluation on a corrupted version of the TIMIT test set and the UCLASS dataset shows significant improvements.
• Score: 10.283092375534311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The study of speech disorders can benefit greatly from time-aligned data. However, audio-text mismatches in disfluent speech cause rapid performance degradation for modern speech aligners, hindering the use of automatic approaches. In this work, we propose a simple and effective modification of alignment graph construction of CTC-based models using Weighted Finite State Transducers. The proposed weakly-supervised approach alleviates the need for verbatim transcription of speech disfluencies for forced alignment. During the graph construction, we allow the modeling of common speech disfluencies, i.e. repetitions and omissions. Further, we show that by assessing the degree of audio-text mismatch through the use of Oracle Error Rate, our method can be effectively used in the wild. Our evaluation on a corrupted version of the TIMIT test set and the UCLASS dataset shows significant improvements, particularly for recall, achieving a 23-25% relative improvement over our baselines.

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