Generative linguistic representation for spoken language identification
Peng Shen Xuguang Lu Hisashi Kawai
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.