Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connec-tionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output align-ment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cur-sive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper in-vestigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the f...
: In this paper, we review the research work that deal with neural network based speech recognition ...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designe...
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Deep neural networks have advanced the state-of-the-art in automatic speech recognition, when combin...
Speech Recognition is correctly transcribing the spoken utterances by the machine. A new area that i...
Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becomin...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
Ebru Arısoy (MEF Author)##nofulltext##Recurrent neural network language models have enjoyed great su...
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for aut...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
The goal of this thesis is to advance the use of recurrent neural network language models (RNNLMs) ...
In this paper, we investigate phone sequence modeling with recurrent neural networks in the context ...
: In this paper, we review the research work that deal with neural network based speech recognition ...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designe...
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Deep neural networks have advanced the state-of-the-art in automatic speech recognition, when combin...
Speech Recognition is correctly transcribing the spoken utterances by the machine. A new area that i...
Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becomin...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
Ebru Arısoy (MEF Author)##nofulltext##Recurrent neural network language models have enjoyed great su...
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for aut...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
The goal of this thesis is to advance the use of recurrent neural network language models (RNNLMs) ...
In this paper, we investigate phone sequence modeling with recurrent neural networks in the context ...
: In this paper, we review the research work that deal with neural network based speech recognition ...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...