Year: 2024
Type: conference-paper
Abstract: We explore the recently proposed explainable acoustic neural embedding (XANE) system that models the background acoustics of a speech signal in a non-intrusive manner. The XANE embeddings are used to estimate specific parameters related to the background acoustic properties of the signal which allows the embeddings to be explainable in terms of those parameters. We perform ablation studies on the XANE system and show that estimating all acoustic parameters jointly has an overall positive effect. Furthermore, we illustrate the value of XANE embeddings by performing clustering experiments on unseen test data and show that the proposed embeddings achieve a mean F1 score of 92% for three different tasks, outperforming significantly the WavLM based signal embeddings and are complimentary to speaker embeddings.
Authors: Dushyant Sharma, James Fosburgh, Sri Harsha Dumpala, Chandramouli Shama Sastri, Stanislav Yu. Kruchinin, Patrick A. Naylor
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403126367