46 citations · 177 across the 28 of their papers we have counts for
31 papers
Acoustic Word Embeddings for Untranscribed Target Languages with Continued Pretraining and Learned Pooling
Ramon Sanabria, Ondrej Klejch, Hao Tang +1
Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units. For unsupervised systems, these are mined using k-nearest neighbor (KN…
Attributable and Scalable Opinion Summarization
Tom Hosking, Hao Tang, Mirella Lapata
We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions b…
Self-supervised Predictive Coding Models Encode Speaker and Phonetic Information in Orthogonal Subspaces
Oli Liu, Hao Tang, Sharon Goldwater
Self-supervised speech representations are known to encode both speaker and phonetic information, but how they are distributed in the high-dimensional space remains largely unexplo…
Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers
Tzu-Quan Lin, Tsung-Huan Yang, Chun-Yao Chang +4
Transformer-based self-supervised models have achieved remarkable success in speech processing, but their large size and high inference cost present significant challenges for real…
MelHuBERT: A simplified HuBERT on Mel spectrograms
Tzu-Quan Lin, Hung-yi Lee, Hao Tang
Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a lar…
Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models
Sung-Lin Yeh, Hao Tang
While discrete latent variable models have had great success in self-supervised learning, most models assume that frames are independent. Due to the segmental nature of phonemes in…