Generative linguistic representation for spoken language identification
This work addresses language identification for speech processing applications, but it is incremental as it adapts existing models to a specific task.
The paper tackled improving spoken language identification by using the Whisper model's decoder to extract linguistic features, achieving enhanced classification accuracy on multilingual datasets like MLS, VoxLingua107, and CommonVoice.
Effective extraction and application of linguistic features are central to the enhancement of spoken Language IDentification (LID) performance. With the success of recent large models, such as GPT and Whisper, the potential to leverage such pre-trained models for extracting linguistic features for LID tasks has become a promising area of research. In this paper, we explore the utilization of the decoder-based network from the Whisper model to extract linguistic features through its generative mechanism for improving the classification accuracy in LID tasks. We devised two strategies - one based on the language embedding method and the other focusing on direct optimization of LID outputs while simultaneously enhancing the speech recognition tasks. We conducted experiments on the large-scale multilingual datasets MLS, VoxLingua107, and CommonVoice to test our approach. The experimental results demonstrated the effectiveness of the proposed method on both in-domain and out-of-domain datasets for LID tasks.