Papers by Egor Lakomkin
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos (D18-2)
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| Challenge: | KT-Speech-Crawler is an automated dataset building tool for speech recognition. |
| Approach: | They propose an approach for automatic dataset construction for speech recognition by crawling YouTube videos. |
| Outcome: | The proposed algorithm can obtain 150 hours of transcribed speech in a day with an estimated 3.5% word error rate. |
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs (2024.naacl-long)
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Yassir Fathullah, Chunyang Wu, Egor Lakomkin, Ke Li, Junteng Jia, Yuan Shangguan, Jay Mahadeokar, Ozlem Kalinli, Christian Fuegen, Mike Seltzer
| Challenge: | a new model for speech processing and reasoning uses curated data instead of text. |
| Approach: | They extend the instruction-tuned Llama-2 model with end-to-end speech processing and reasoning abilities without using any carefully curated paired data. |
| Outcome: | The proposed model outperforms or outperfects existing models on synthesized and recorded speech QA tests. |