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 zeroshot 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.
Original language | English |
---|---|
Publication status | Published - 18 Mar 2024 |
Event | 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of Duration: 14 Apr 2024 → 19 Apr 2024 |
Conference
Conference | 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 |
---|---|
Country/Territory | Korea, Republic of |
City | Seoul |
Period | 14/04/24 → 19/04/24 |