3 citations · 6 across the 7 of their papers we have counts for
7 papers
A Simple HMM with Self-Supervised Representations for Phone Segmentation
Gene-Ping Yang, Hao Tang
Despite the recent advance in self-supervised representations, unsupervised phonetic segmentation remains challenging. Most approaches focus on improving phonetic representations w…
Towards Matching Phones and Speech Representations
Gene-Ping Yang, Hao Tang
Learning phone types from phone instances has been a long-standing problem, while still being open. In this work, we revisit this problem in the context of self-supervised learning…
On-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation
Gene-Ping Yang, Yue Gu, Qingming Tang +2
Large self-supervised models are effective feature extractors, but their application is challenging under on-device budget constraints and biased dataset collection, especially in…
Supervised Attention in Sequence-to-Sequence Models for Speech Recognition
Gene-Ping Yang, Hao Tang
Attention mechanism in sequence-to-sequence models is designed to model the alignments between acoustic features and output tokens in speech recognition. However, attention weights…
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Sung-Feng Huang, Shun-Po Chuang, Da-Rong Liu +3
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during…
Interrupted and cascaded permutation invariant training for speech separation
Gene-Ping Yang, Szu-Lin Wu, Yao-Wen Mao +2
Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the mini…