Unified Speech-To-Speech Models for Real-Time, Multilingual, and Emotionally Aware AI

Abstract

Vansh Kumar* and M Tanusri

This paper presents a novel speech-to-speech (S2S), a 250 Billion parameter AI model built on a multimodal AI foundation, Vision [16]. The model is trained to natively understand and generate speech while preserving prosody, emotional nuance, and speaker-specific characteristics, enabling fully end-to-end, real-time conversational interactions. Unlike traditional cascaded systems that rely on separate ASR, LLM, and TTS components, our model integrates speech understanding, reasoning, and generation within a unified framework, minimizing latency and mitigating error propagation. The system is trained on 400,000+ hours of multilingual conversational and expressive speech, supporting over 200 languages, including all major Indian languages, and is capable of cross-lingual prosody adaptation. Evaluations on extensive benchmarks demonstrate state-of-the-art performance in technical reasoning, ethical alignment, emotional expressiveness, multilingual fluency, and experiential learning capabilities. By combining real- time responsiveness, contextual reasoning, and human-like expressiveness, this S2S model represents a significant step toward scalable, culturally aware, and emotionally intelligent conversational AI systems, with potential applications ranging from empathetic customer support to multilingual communication and technical assistance.

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