Unsupervised Cross lingual Representation Learning for Speech Recognition
This paper presents XLSR which learns cross lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on a concurrently introduced self supervised model which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine tuned on labeled data and experiments show that cross lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to the strongest comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages.
Interspeech 2021