Audio Samples from "MambaVoiceCloning: Efficient and Expressive Text-to-Speech via State-Space Modeling and Diffusion Control"

Abstract: This paper presents MambaVoiceCloning MVC, a scalable and expressive text-to-speech (TTS) framework that unifies state-space sequence modeling with diffusion-driven style control. Distinct from prior diffusion-based models, MVC replaces all self-attention and recurrent components in the TTS pipeline with novel Mamba-based modules: a Bi-Mamba Text Encoder, Temporal Bi-Mamba Encoder, and Expressive Mamba Predictor. These modules enable linear-time modeling of long-range phonetic and prosodic dependencies, improving efficiency and expressiveness without relying on external reference encoders. While MVC uses a diffusion-based decoder for waveform generation, our contribution is architectural—introducing the first end-to-end Mamba-integrated TTS backbone. Extensive experiments on LJSpeech and LibriTTS demonstrate that MVC significantly improves naturalness, prosody, intelligibility, and latency over state-of-the-art methods. MVC maintains a lightweight footprint of 21M parameters and achieves 1.6× faster training than comparable Transformer-based baselines.