arXiv:2607.08208v1 Announce Type: new Abstract: This paper describes our self-designed system for Task 1 of the MLC-SLM 2026 Challenge for multilingual two-speaker conversational speech. The system combines a modular speaker diarization front end with a challenge-adapted Qwen3-ASR-1.7B recognizer. The diarization front end performs voice activity detection, subsegment generation, CAMPPlus speaker embedding extraction, two-speaker spectral clustering, and RTTM-based audio segmentation. The resulting speaker-attributed segments are grouped by language or region and decoded by the adapted ASR mod
The proliferation of complex, multilingual audio data and the ongoing development of advanced ASR models create a critical need for efficient and accurate speaker diarization techniques.
This development improves speech-to-text accuracy in multi-speaker, multilingual conversations, essential for applications across AI agents, customer service, and intelligence gathering.
The ability to accurately attribute spoken words to specific speakers in complex audio environments becomes more robust, enhancing the utility of ASR systems in real-world scenarios.
- · AI developers
- · Customer service platforms
- · Multilingual communication tools
- · Security and intelligence agencies
Improved transcription accuracy for multi-speaker, multilingual audio.
Accelerated development of AI agent applications that rely on nuanced conversational understanding.
Enhanced automation of tasks involving complex human interactions, leading to productivity gains in white-collar sectors.
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