Human-1 by Josh Talks: A Full-Duplex Conversational Modeling Framework in Hindi using Real-World Conversations
arXiv:2604.23295v2 Announce Type: replace Abstract: Full-duplex spoken dialogue systems can model natural conversational behaviours such as interruptions, overlaps, and backchannels, yet such systems remain largely unexplored for Indian languages. We present the first open, reproducible full-duplex spoken dialogue system for Hindi by adapting Moshi, a state-of-the-art duplex speech architecture, using a custom Hindi tokeniser and training on 26,000 hours of real spontaneous conversations collected from 14,695 speakers with separate speaker channels, enabling direct learning of turn-taking and
The proliferation of AI research in diverse languages, coupled with the increasing availability of large, real-world conversational datasets, enables the development of sophisticated, culturally relevant AI models.
This marks a significant step towards democratizing advanced AI conversational capabilities for non-English speaking populations, specifically addressing Hindi speakers and setting a precedent for other Indian languages.
The previous dependency on English-centric full-duplex conversational models for state-of-the-art interaction is challenged by the emergence of high-quality, localized alternatives.
- · India's AI sector
- · Hindi-speaking population
- · Local language AI developers
- · Emerging market AI consumers
- · Monolingual AI service providers
- · Generic, non-localised conversational AI platforms
Increased adoption of AI tools and services among Hindi speakers due to more natural and effective interaction.
Accelerated development of full-duplex conversational systems for other Indian and non-Western languages, reducing AI dependency on English and Western tech stacks.
Enhanced digital inclusion and economic participation for vast populations previously underserved by advanced AI, potentially fostering new markets and applications rooted in local linguistic and cultural contexts.
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