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New dataset and model improve Tamil speech turn detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TamilEOT, a new dataset and model for detecting the end of spoken turns in Tamil telephone conversations. This system aims to improve voice agent interactions by accurately determining when a user has finished speaking, rather than relying on fixed silence timeouts. The dataset comprises over 18,000 labeled turn boundaries from real conversations, and the fine-tuned models achieve an accuracy of over 86%. The project also details the cost and methodology involved in creating the dataset and models, emphasizing the impact of encoder capacity and data labeling on performance. AI

IMPACT Improves voice agent responsiveness and accuracy in Tamil language interactions.

RANK_REASON The item describes a new dataset and model for a specific NLP task (semantic end-of-turn detection) published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and model improve Tamil speech turn detection

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The item describes a new dataset and model for a specific NLP task (semantic end-of-turn detection) published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Santhoshkumar V ·

    TamilEOT: A Dataset and Model for Semantic End-of-Turn Detection in Tamil Telephone Speech

    arXiv:2609.05631v1 Announce Type: cross Abstract: A voice agent has to decide, at every pause, whether the user has finished speaking. Without a model of the language that decision falls back to a fixed silence timeout: set it short and the agent interrupts, set it long and every…

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