MoLE : Mixture Of Language Experts For Multi-Lingual Automatic Speech Recognition

Multi-lingual speech recognition aims to distinguish linguistic expressions in different languages and integrate acoustic processing simultaneously. In contrast, current multilingual speech recognition research follows a language-aware paradigm, mainly targeted to improve recognition performance rather than discriminate language characteristics. In this paper, we present a multi-lingual speech recognition network named Mixture-of-Language-Experts (MoLE), which digests speech in a variety of languages. Specifically, MoLE analyzes linguistic expression from input speech in arbitrary languages, activating a language-specific expert with a lightweight language gating network. The gating network not only activates experts, but also estimates the reliability of the activation. Based on the reliability, the activated expert and the language-agnostic expert are aggregated to represent language-conditioned embedding for efficient speech recognition. Our proposed model is evaluated in 5 languages scenario, and the experimental results show that our structure is advantageous on multi-lingual recognition, especially for speech in low-resource language.