25 citations · 33 across the 5 of their papers we have counts for
5 papers
Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition
Jinxi Guo, Gautam Tiwari, Jasha Droppo +4
In this work, we propose a novel and efficient minimum word error rate (MWER) training method for RNN-Transducer (RNN-T). Unlike previous work on this topic, which performs on-the-…
Streaming ResLSTM with Causal Mean Aggregation for Device-Directed Utterance Detection
Xiaosu Tong, Che-Wei Huang, Sri Harish Mallidi +5
In this paper, we propose a streaming model to distinguish voice queries intended for a smart-home device from background speech. The proposed model consists of multiple CNN layers…
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition
Che-Wei Huang, Shrikanth S. Narayanan
Regularization is crucial to the success of many practical deep learning models, in particular in a more often than not scenario where there are only a few to a moderate number of…
Shaking Acoustic Spectral Sub-bands Can Better Regularize Learning in Affective Computing
Che-Wei Huang, Shrikanth Narayanan
In this work, we investigate a recently proposed regularization technique based on multi-branch architectures, called Shake-Shake regularization, for the task of speech emotion rec…
Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition
Che-Wei Huang, Shrikanth. S. Narayanan
Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection a…