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Fréchet Distance Loss on Speech Representations for Text-to-Speech Synthesis
Ho-Lam Chung, Kuan-Po Huang, Bo-Ru Lu +1
Few-step diffusion and flow-matching text-to-speech (TTS) models are usually trained with local objectives, such as conditional flow matching, reconstruction, and stop prediction.…
Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation
Kuan-Po Huang, Bo-Ru Lu, Byeonggeun Kim +8
Autoregressive (AR) models with diffusion heads have recently achieved strong text-to-audio performance, yet their iterative decoding and multi-step sampling process introduce high…
The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era
Zhixian Zhao, Shuiyuan Wang, Guojian Li +12
Driven by the rapid advancement of Large Language Models (LLMs), particularly Audio-LLMs and Omni-models, spoken dialogue systems have evolved significantly, progressively narrowin…
Dataset-Distillation Generative Model for Speech Emotion Recognition
Fabian Ritter-Gutierrez, Kuan-Po Huang, Jeremy H. M Wong +4
Deep learning models for speech rely on large datasets, presenting computational challenges. Yet, performance hinges on training data size. Dataset Distillation (DD) aims to learn…
Noise robust distillation of self-supervised speech models via correlation metrics
Fabian Ritter-Gutierrez, Kuan-Po Huang, Dianwen Ng +4
Compared to large speech foundation models, small distilled models exhibit degraded noise robustness. The student's robustness can be improved by introducing noise at the inputs du…