Combining dual encoders, LID-routed MoE LoRA, and CTC prompts yields top challenge results for multilingual conversational ASR and speech diarization.
Golos: Russian Dataset for Speech Research
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abstract
This paper introduces a novel Russian speech dataset called Golos, a large corpus suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours. We have made the corpus freely available to download, along with the acoustic model with CTC loss prepared on this corpus. Additionally, transfer learning was applied to improve the performance of the acoustic model. In order to evaluate the quality of the dataset with the beam-search algorithm, we have built a 3-gram language model on the open Common Crawl dataset. The total word error rate (WER) metrics turned out to be about 3.3% and 11.5%.
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The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge
Combining dual encoders, LID-routed MoE LoRA, and CTC prompts yields top challenge results for multilingual conversational ASR and speech diarization.