1 citations · 1 across the 2 of their papers we have counts for
4 papers · 1 filter
A Neural State-Space Model Approach to Efficient Speech Separation
Chen Chen, Chao-Han Huck Yang, Kai Li +3
In this work, we introduce S4M, a new efficient speech separation framework based on neural state-space models (SSM). Motivated by linear time-invariant systems for sequence modeli…
Differentially Private Adapters for Parameter Efficient Acoustic Modeling
Chun-Wei Ho, Chao-Han Huck Yang, Sabato Marco Siniscalchi
In this work, we devise a parameter-efficient solution to bring differential privacy (DP) guarantees into adaptation of a cross-lingual speech classifier. We investigate a new froz…
Parameter-Efficient Learning for Text-to-Speech Accent Adaptation
Li-Jen Yang, Chao-Han Huck Yang, Jen-Tzung Chien
This paper presents a parameter-efficient learning (PEL) to develop a low-resource accent adaptation for text-to-speech (TTS). A resource-efficient adaptation from a frozen pre-tra…
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition
Chao-Han Huck Yang, I-Fan Chen, Andreas Stolcke +2
Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise pertu…