Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions

Autor:	Xu, Anfeng, Huang, Kevin, Feng, Tiantian, Shen, Lue, Tager-Flusberg, Helen, Narayanan, Shrikanth
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Speech foundation models, trained on vast datasets, have opened unique opportunities in addressing challenging low-resource speech understanding, such as child speech. In this work, we explore the capabilities of speech foundation models on child-adult speaker diarization. We show that exemplary foundation models can achieve 39.5% and 62.3% relative reductions in Diarization Error Rate and Speaker Confusion Rate, respectively, compared to previous speaker diarization methods. In addition, we benchmark and evaluate the speaker diarization results of the speech foundation models with varying the input audio window size, speaker demographics, and training data ratio. Our results highlight promising pathways for understanding and adopting speech foundation models to facilitate child speech understanding. Comment: Interspeech 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.07890 Zobrazit plný text záznamu View this record from Arxiv