Deep recurrent neural networks were recently shown to give state-of-the-art performance in phoneme recognition on the TIMIT database [1]. However these results relied on end-to-end training methods that are difficult to integrat
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Abstract Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. Howe...
Ebru Arısoy (MEF Author)##nofulltext##Recurrent neural network language models have enjoyed great su...
Deep Bidirectional LSTM (DBLSTM) recurrent neural net-works have recently been shown to give state-o...
Abstract. In this paper, we carry out two experiments on the TIMIT speech cor-pus with bidirectional...
This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (L...
One possible explanation for RNN language models' outsized effectiveness in voice recognition is its...
Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional...
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training metho...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
Abstract — In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a mod...
This paper presents a speech recognition sys-tem that directly transcribes audio data with text, wit...
We apply Long Short-Term Memory (LSTM) recurrent neural networks to a large corpus of unprompted spe...
: In this paper, we review the research work that deal with neural network based speech recognition ...
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Abstract Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. Howe...
Ebru Arısoy (MEF Author)##nofulltext##Recurrent neural network language models have enjoyed great su...
Deep Bidirectional LSTM (DBLSTM) recurrent neural net-works have recently been shown to give state-o...
Abstract. In this paper, we carry out two experiments on the TIMIT speech cor-pus with bidirectional...
This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (L...
One possible explanation for RNN language models' outsized effectiveness in voice recognition is its...
Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional...
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training metho...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state...
Abstract — In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a mod...
This paper presents a speech recognition sys-tem that directly transcribes audio data with text, wit...
We apply Long Short-Term Memory (LSTM) recurrent neural networks to a large corpus of unprompted spe...
: In this paper, we review the research work that deal with neural network based speech recognition ...
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-...
Abstract Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. Howe...
Ebru Arısoy (MEF Author)##nofulltext##Recurrent neural network language models have enjoyed great su...
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