Lightweight and Efficient Spoken Language Identification of Long-form Audio


State-of-the-art Spoken Language Identification (SLI) systems usually focus on tackling short audio clips, and thus their performance degrade drastically when applied to long-form audio, such as podcast, which poses peculiar challenges to existing SLI approaches due to its long duration and diverse content that frequently involves multiple speakers as well as various languages, topics, and speech styles. In this paper, we propose the first system to tackle SLI for long-form audio using podcast data by training a lightweight, multi-class feedforward neural classifier using speaker embeddings as input. We demonstrate that our approach can make inference on long audio input efficiently; furthermore, our system can handle long audio files with multiple speakers and can be further extended into utterance-level inference and code-switching detection, which is currently not covered by any existing SLI system.


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