Adaptive Oscillatory Inductive Bias for Modeling Sharp Prosodic Dynamics in Diffusion-Based TTS
arXiv:2606.25424v1 Announce Type: cross Abstract: Diffusion-based text-to-speech (TTS) models have achieved significant improvements in speech quality. However, modeling sharp prosodic transitions and rapid pitch variations in expressive speech remains challenging. Existing diffusion-based TTS decoders commonly utilize periodic nonlinearities such as Snake activation function to capture harmonic structures, but this activation funcation provides limited adaptability when modeling abrupt amplitude and frequency variations. In this paper, we investigate the role of oscillatory inductive bias in
The continuous improvement in AI models for speech generation, specifically text-to-speech (TTS), drives ongoing research into overcoming current limitations for more natural and expressive outputs.
Improving the naturalness and expressiveness of AI-generated speech is crucial for broader adoption in various applications, enhancing user experience and human-computer interaction.
Advancements in modeling sharp prosodic dynamics in diffusion-based TTS could lead to more nuanced and emotionally resonant AI voices, moving beyond current robotic or monotonous outputs.
- · AI Speech Synthesis Developers
- · Content Creators
- · Accessibility Tech
- · Virtual Assistants
- · Monotone TTS Systems
Higher quality AI voices enable more engaging and believable virtual characters and digital interfaces.
The improved realism of synthetic speech may blur the lines between human and AI voices, raising ethical and identification challenges.
Sophisticated voice synthesis could lead to new forms of entertainment, education, and communication, personalized to individual preferences.
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