32 citations · 54 across the 5 of their papers we have counts for
5 papers
A Treatise On FST Lattice Based MMI Training
Adnan Haider, Tim Ng, Zhen Huang +2
Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models. This paper aims to isolate, identify…
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching
Zhen Huang, Xiaodan Zhuang, Daben Liu +3
In this paper, we present our initial efforts for building a code-switching (CS) speech recognition system leveraging existing acoustic models (AMs) and language models (LMs), i.e.…
SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition
Zhen Huang, Tim Ng, Leo Liu +3
Very deep CNNs achieve state-of-the-art results in both computer vision and speech recognition, but are difficult to train. The most popular way to train very deep CNNs is to use s…
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
Yong Xu, Jun Du, Zhen Huang +2
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-…
Maximum a Posteriori Adaptation of Network Parameters in Deep Models
Zhen Huang, Sabato Marco Siniscalchi, I-Fan Chen +2
We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recogni…