Add to ORKGIn this paper, we investigate semi-supervised training (SST) method in various state-of-the-art acoustic modeling tech-niques, using bottle-neck and corresponding tandem features. These techniques include subspace GMM, tanh-neuron deep neural network (DNN), and a generalized soft-maxout (p-norm) DNN. We demonstrate that SST may lead up to 2 % Word Error Rate (WER) reduction using all these techniques in each case, and the best one comes from tandem feature based p-norm DNN system. In addition to recognition performance, effectiveness of the SST on keyword search performance is also investigated. Results on Actual Term Weighted Value (ATWV) are reported, with an analysis on lattice density. It is shown that SST may not necessarily increase A...
ABSTRACT Hidden Markov model speech recognition systems typically use Gaussian mixture models to est...
Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significa...
We investigate two strategies to improve the context-dependent deep neural network hidden Markov mod...
In this paper we propose a Shared Hidden Layer Multi-softmax Deep Neural Network (SHL-MDNN) approach...
Training acoustic models for ASR requires large amounts of labelled data which is costly to obtain. ...
Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are the state-of-the-art for acoustic modelin...
Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-ar...
Abstract We present our study on semi-supervised Gaussian mixture model (GMM) hidden Markov model (H...
We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-end...
In this work, we propose a modular combination of two pop-ular applications of neural networks to la...
Training acoustic models for ASR requires large amounts of labelled data which is costly to obtain. ...
We investigate two strategies to improve the context-dependent deep neural network hidden Markov mod...
Recently, context-dependent (CD) deep neural network (DNN) hidden Markov models (HMMs) have been wid...
We describe a novel way to implement subword language models in speech recognition systems based on ...
In this paper, we discuss some of the properties of training acoustic models using a lattice-free ve...
ABSTRACT Hidden Markov model speech recognition systems typically use Gaussian mixture models to est...
Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significa...
We investigate two strategies to improve the context-dependent deep neural network hidden Markov mod...
In this paper we propose a Shared Hidden Layer Multi-softmax Deep Neural Network (SHL-MDNN) approach...
Training acoustic models for ASR requires large amounts of labelled data which is costly to obtain. ...
Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are the state-of-the-art for acoustic modelin...
Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-ar...
Abstract We present our study on semi-supervised Gaussian mixture model (GMM) hidden Markov model (H...
We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-end...
In this work, we propose a modular combination of two pop-ular applications of neural networks to la...
Training acoustic models for ASR requires large amounts of labelled data which is costly to obtain. ...
We investigate two strategies to improve the context-dependent deep neural network hidden Markov mod...
Recently, context-dependent (CD) deep neural network (DNN) hidden Markov models (HMMs) have been wid...
We describe a novel way to implement subword language models in speech recognition systems based on ...
In this paper, we discuss some of the properties of training acoustic models using a lattice-free ve...
ABSTRACT Hidden Markov model speech recognition systems typically use Gaussian mixture models to est...
Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significa...
We investigate two strategies to improve the context-dependent deep neural network hidden Markov mod...