Add to ORKGHybrid systems which integrate the deep neural network (DNN) and hidden Markov model (HMM) have recently achieved re-markable performance in many large vocabulary speech recog-nition tasks. These systems, however, remain to rely on the HMM and assume the acoustic scores for the (windowed) frames are independent given the state, suffering from the same difficulty as in the previous GMM-HMM systems. In this pa-per, we propose the deep segmental neural network (DSNN), a segmental model that uses DNNs to estimate the acoustic scores of phonemic or sub-phonemic segments with variable lengths. This allows the DSNN to represent each segment as a single unit, in which frames are made dependent on each other. We describe the architecture of the DSNN...
Recently, convolutional neural networks (CNNs) have been shown to outperform the standard fully conn...
Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recogn...
We describe a novel way to implement subword language models in speech recognition systems based on ...
We present he concept of a "Segmental Neural Net " (SNN) for phonetic modeling in continuo...
In an effort to advance the state of the art in continuous peech recognition employing hidden Markov...
In this work, we propose a modular combination of two pop-ular applications of neural networks to la...
Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significa...
Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are the state-of-the-art for acoustic modelin...
In recent years, researchers have established the viability of so called hybrid NN/HMM large vocabul...
Neural networks, especially those with more than one hidden layer, have re-emerged in Automatic Spee...
Recently, deep learning techniques have been successfully applied to automatic speech recognition (A...
This paper describes a hybrid system for continuous speech recognition consisting of a neural networ...
Hybrid deep neural network-hidden Markov model (DNN-HMM) systems have become the state-of-the-art in...
Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-ar...
The Segmental Neural Network (SNN) architecture was introduced at BBN by Zavaliagkos et al. for resc...
Recently, convolutional neural networks (CNNs) have been shown to outperform the standard fully conn...
Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recogn...
We describe a novel way to implement subword language models in speech recognition systems based on ...
We present he concept of a "Segmental Neural Net " (SNN) for phonetic modeling in continuo...
In an effort to advance the state of the art in continuous peech recognition employing hidden Markov...
In this work, we propose a modular combination of two pop-ular applications of neural networks to la...
Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significa...
Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are the state-of-the-art for acoustic modelin...
In recent years, researchers have established the viability of so called hybrid NN/HMM large vocabul...
Neural networks, especially those with more than one hidden layer, have re-emerged in Automatic Spee...
Recently, deep learning techniques have been successfully applied to automatic speech recognition (A...
This paper describes a hybrid system for continuous speech recognition consisting of a neural networ...
Hybrid deep neural network-hidden Markov model (DNN-HMM) systems have become the state-of-the-art in...
Hidden Markov models (HMMs) have been the mainstream acoustic modelling approach for state-of-the-ar...
The Segmental Neural Network (SNN) architecture was introduced at BBN by Zavaliagkos et al. for resc...
Recently, convolutional neural networks (CNNs) have been shown to outperform the standard fully conn...
Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recogn...
We describe a novel way to implement subword language models in speech recognition systems based on ...