86 citations · 363 across the 30 of their papers we have counts for
5 papers · 1 filter
A Study of All-Convolutional Encoders for Connectionist Temporal Classification
Kalpesh Krishna, Liang Lu, Kevin Gimpel +1
Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural…
End-to-End Neural Segmental Models for Speech Recognition
Hao Tang, Liang Lu, Lingpeng Kong +5
Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at…
Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations
Liang Lu
Neural network acoustic models have significantly advanced state of the art speech recognition over the past few years. However, they are usually computationally expensive due to t…
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
Shubham Toshniwal, Hao Tang, Liang Lu +1
End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignore…
Multitask Learning with CTC and Segmental CRF for Speech Recognition
Liang Lu, Lingpeng Kong, Chris Dyer +1
Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition mod…