Speech collage: code-switched audio generation by collaging monolingual corpora

• URL: http://arxiv.org/abs/2309.15674v1
• Date: Wed, 27 Sep 2023 14:17:53 GMT
• Title: Speech collage: code-switched audio generation by collaging monolingual corpora
• Authors: Amir Hussein, Dorsa Zeinali, Ond\v{r}ej Klejch, Matthew Wiesner, Brian Yan, Shammur Chowdhury, Ahmed Ali, Shinji Watanabe, Sanjeev Khudanpur
• Abstract summary: Speech Collage is a method that synthesizes CS data from monolingual corpora by splicing audio segments. We investigate the impact of generated data on speech recognition in two scenarios.
• Score: 50.356820349870986
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Designing effective automatic speech recognition (ASR) systems for Code-Switching (CS) often depends on the availability of the transcribed CS resources. To address data scarcity, this paper introduces Speech Collage, a method that synthesizes CS data from monolingual corpora by splicing audio segments. We further improve the smoothness quality of audio generation using an overlap-add approach. We investigate the impact of generated data on speech recognition in two scenarios: using in-domain CS text and a zero-shot approach with synthesized CS text. Empirical results highlight up to 34.4% and 16.2% relative reductions in Mixed-Error Rate and Word-Error Rate for in-domain and zero-shot scenarios, respectively. Lastly, we demonstrate that CS augmentation bolsters the model's code-switching inclination and reduces its monolingual bias.

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