102 citations · 188 across the 21 of their papers we have counts for
11 papers · 1 filter
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering
Heng-Jui Chang, Alexander H. Liu, James Glass
Self-supervised speech representation models have succeeded in various tasks, but improving them for content-related problems using unlabeled data is challenging. We propose speake…
DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning
Alexander H. Liu, Heng-Jui Chang, Michael Auli +2
In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distil…
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation
Alexander Liu, Samuel Yang
Despite the progresses on pre-trained language models, there is a lack of unified frameworks for pre-trained sentence representation. As such, it calls for different pre-training m…
Towards End-to-end Unsupervised Speech Recognition
Alexander H. Liu, Wei-Ning Hsu, Michael Auli +1
Unsupervised speech recognition has shown great potential to make Automatic Speech Recognition (ASR) systems accessible to every language. However, existing methods still heavily r…
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu +7
Self-supervised speech representation learning (speech SSL) has demonstrated the benefit of scale in learning rich representations for Automatic Speech Recognition (ASR) with limit…
Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies
Alexander H. Liu, Yu-An Chung, James Glass
Self-supervised speech representations have been shown to be effective in a variety of speech applications. However, existing representation learning methods generally rely on the…
Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
Shun-Po Chuang, Tzu-Wei Sung, Alexander H. Liu +1
Speech translation (ST) aims to learn transformations from speech in the source language to the text in the target language. Previous works show that multitask learning improves th…
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training
Heng-Jui Chang, Alexander H. Liu, Hung-yi Lee +1
Whispering is an important mode of human speech, but no end-to-end recognition results for it were reported yet, probably due to the scarcity of available whispered speech data. In…
Sequence-to-sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding
Alexander H. Liu, Tzu-Wei Sung, Shun-Po Chuang +2
In this paper, we investigate the benefit that off-the-shelf word embedding can bring to the sequence-to-sequence (seq-to-seq) automatic speech recognition (ASR). We first introduc…
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning
Alexander H. Liu, Tao Tu, Hung-yi Lee +1
In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very c…
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model
Alexander H. Liu, Hung-yi Lee, Lin-shan Lee
In this paper we proposed a novel Adversarial Training (AT) approach for end-to-end speech recognition using a Criticizing Language Model (CLM). In this way the CLM and the automat…