Human-1 by Josh Talks: A Full-Duplex Conversational Modeling Framework in Hindi using Real-World Conversations

• URL: http://arxiv.org/abs/2604.23295v1
• Date: Sat, 25 Apr 2026 13:18:40 GMT
• Title: Human-1 by Josh Talks: A Full-Duplex Conversational Modeling Framework in Hindi using Real-World Conversations
• Abstract summary: Full spoken dialogue systems can model natural conversational behaviours such as interruptions overlaps and back-tuning.<n>This work serves as a first step toward real-time spoken dialogue systems for Hindi and other Indian languages.
• Score: 1.218012293738896
• License: http://creativecommons.org/licenses/by/4.0/
• 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 overlap patterns from natural interactions. To support Hindi text generation, we replace the original English tokeniser and reinitialise text-vocabulary-dependent parameters while retaining the pre-trained audio components. We propose a two-stage training recipe -- large-scale pre-training followed by fine-tuning on 1,000 hours of conversational data. Evaluation through the prompted dialogue continuation paradigm with both automatic metrics and human judgments demonstrates that the resulting model generates natural and meaningful full-duplex conversational behaviour in Hindi. This work serves as a first step toward real-time duplex spoken dialogue systems for Hindi and other Indian languages.

Related papers

• F-Actor: Controllable Conversational Behaviour in Full-Duplex Models [70.48189107402145]
  We present first open, instruction-following full-stage conversational speech model that can be trained efficiently under typical academic resource constraints.<n>Our model requires just 2,000 hours of data, without relying on large-scale pretraining or multi-stage pretraining.<n>Both the model and training code will be released to enable reproducible research on controllable full-like controllable full-stage speech systems.
  arXiv  Detail & Related papers  (2026-01-16T14:25:57Z)
• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• Towards a Japanese Full-duplex Spoken Dialogue System [8.984488716637655]
  Full spoken dialogue systems have attracted significant attention recently.<n>In this paper we present first publicly available full-stage spoken dialogue model in Japanese.<n>Our model is trained through two-channel process: pre-training on a large-scale spoken dialogue data in Japanese, followed by fine-tuning on high-quality stereo spoken dialogue data.
  arXiv  Detail & Related papers  (2025-06-03T15:16:50Z)
• OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation [53.7173034249361]
  End-to-end GPT-based model OmniFlatten capable of effectively modeling complex behaviors inherent natural conversations with low latency.<n>Our approach offers a simple modeling technique and a promising research direction for developing efficient and natural end-to-end full- spoken dialogue systems.
  arXiv  Detail & Related papers  (2024-10-23T11:58:58Z)
• Moshi: a speech-text foundation model for real-time dialogue [78.88479749811376]
  Current systems for spoken dialogue rely on pipelines independent voice activity detection and text-to-speech. We show how Moshi Moshi can provide streaming speech recognition and text-to-speech. Our resulting model is first real-time full spoken large language model modality.
  arXiv  Detail & Related papers  (2024-09-17T17:55:39Z)
• Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model [47.67067056593085]
  We develop a pipeline capable of transforming single-channel dialogue data into pseudo-stereo data. This expanded our training dataset from a mere 2,000 to an impressive 17,600 hours. The inclusion of this pseudo-stereo data has proven to be effective in improving the performance of spoken dialogue language models.
  arXiv  Detail & Related papers  (2024-07-02T03:22:41Z)
• Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation [55.043492250775294]
  We introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response. We also introduce MultiDialog, the first large-scale multimodal spoken dialogue corpus.
  arXiv  Detail & Related papers  (2024-06-12T04:48:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.