Publications (39)
Self-training and Pre-training are Complementary for Speech Recognition
Qiantong Xu, Alexei Baevski, Tatiana Likhomanenko +5
Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they l…
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski +8
Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist l…
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…
Toward Joint Language Modeling for Speech Units and Text
Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu +5
Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of lan…
Adaptive Input Representations for Neural Language Modeling
Alexei Baevski, Michael Auli
We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. The…
On-demand compute reduction with stochastic wav2vec 2.0
Apoorv Vyas, Wei-Ning Hsu, Michael Auli +1
Squeeze and Efficient Wav2vec (SEW) is a recently proposed architecture that squeezes the input to the transformer encoder for compute efficient pre-training and inference with wav…