AI BriefingLightweight Real-Time Multilingual ASR Router Released
Key point
It is a technology that achieves high-performance multilingual speech recognition in local environments by routing small monolingual models.
Details
To solve the problems of high resource requirements and low accuracy during language switching in large multilingual models, a new approach is proposed that routes small monolingual models of about 100 million (100M) parameters.
The system utilizes the following components:
- Zipformer: performs low-latency streaming transcription
- Silero VAD: detects speech boundaries
- SpeechBrain: performs language identification (LID)
The way it works is that it immediately starts speech recognition, and when the language identification confidence exceeds a threshold, it uses a self-correction mechanism that goes back to the last speech boundary and re-transcribes with the correct model.
In terms of performance, on the inter-utterance language-switching benchmark, it records WER of about 13%, which is better than the cloud APIs tested. On intra-utterance language switching, WER rises to about 41%, but it is much lighter and more efficient compared to existing open-source alternatives.
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