Speech collage: code-switched audio generation by collaging monolingual
  corpora
Amir Hussein Dorsa Zeinali Ondřej Klejch Matthew Wiesner Brian Yan Shammur Chowdhury Ahmed Ali Shinji Watanabe Sanjeev Khudanpur
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.