5 accepted papers
This paper improves the streaming transformer transducer for speech recognition using non-causal convolution. Many works apply the causal convolution to improve streaming transformer ignoring the lookahead context. We propose to use non-causal convolution to process the center block and lookahead co…
The problem of machine learning systems demonstrating bias towards specific groups of individuals has been studied extensively, particularly in the Facial Recognition area, but much less so in Automatic Speech Recognition (ASR). This paper presents initial Speech Recognition results on “Casual Conve…
Pseudo-labeling is the most adopted method for pre-training automatic speech recognition (ASR) models. However, its performance suffers with degrading quality of the supervised teacher model.Inspired by the successes of contrastive representation learning for both computer vision and speech applicat…
Transformer-based acoustic models have shown promising results very recently. In this paper, we summarize the application of transformer and its streamable variant, Emformer based acoustic model [1] for large scale speech recognition applications. We compare the transformer based acoustic models wit…
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional embedding methods and an iterated loss to enable training deep transformers. We also present a preliminary study of using l…