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arXiv · 2205.10397

Modernizing Open-Set Speech Language Identification

Mustafa Eyceoz·Justin Lee·Homayoon Beigi
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Abstract

While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set problem, the solution gains the ability to reject an audio input when it fails to match any of our known language options. We tackle the open-set task by adapting two modern-day state-of-the-art approaches to closed-set language identification: the first using a CRNN with attention and the second using a TDNN. In addition to enhancing our input feature embeddings using MFCCs, log spectral features, and pitch, we will be attempting two approaches to out-of-set language detection: one using thresholds, and the other essentially performing a verification task. We will compare both the performance of the TDNN and the CRNN, as well as our detection approaches.

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BibTeXRIS

Mustafa Eyceoz, Justin Lee, Homayoon Beigi. 2022-05-20. Modernizing Open-Set Speech Language Identification. https://doi.org/10.13140/rg.2.2.24797.28647

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