Code-Switching Text Generation and Injection in Mandarin-English ASR
This addresses data scarcity for code-switching ASR in an industry setting, but it is incremental as it builds on existing methods like TTS and latent space tying.
The study tackled the problem of improving Mandarin-English code-switching speech recognition by generating and injecting text data into a Transformer-Transducer model, resulting in a 16% relative reduction in Token-based Error Rate averaged across three evaluation sets.
Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance. Automatic Speech Recognition (ASR) with End-to-End (E2E) modeling for such speech can be a challenging task due to the lack of data. In this study, we investigate text generation and injection for improving the performance of an industry commonly-used streaming model, Transformer-Transducer (T-T), in Mandarin-English code-switching speech recognition. We first propose a strategy to generate code-switching text data and then investigate injecting generated text into T-T model explicitly by Text-To-Speech (TTS) conversion or implicitly by tying speech and text latent spaces. Experimental results on the T-T model trained with a dataset containing 1,800 hours of real Mandarin-English code-switched speech show that our approaches to inject generated code-switching text significantly boost the performance of T-T models, i.e., 16% relative Token-based Error Rate (TER) reduction averaged on three evaluation sets, and the approach of tying speech and text latent spaces is superior to that of TTS conversion on the evaluation set which contains more homogeneous data with the training set.