3 papers
eess.AS2026
EmphTTS: an emphasis-control TTS with reinforcement learning
Zirui Li, Rech Silas, Lauri Juvela +2
Generating controllable and human-like emphasis remains an open challenge in text-to-speech, even when explicit emphasis control signals are provided in the text input, limiting th…
cs.SD2026
Performance and Complexity Trade-off Optimization of Speech Models During Training
Esteban Gómez, Tom Bäckström, Tom Backström
In speech machine learning, neural network models are typically designed by choosing an architecture with fixed layer sizes and structure. These models are then trained to maximize…
cs.LG2025
DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick
Mohammad Hassan Vali, Tom Bäckström, Arno Solin
Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treats quantization as adding an erro…