Add to ORKGWe apply Long Short-Term Memory (LSTM) recurrent neural networks to a large corpus of unprompted speech- the German part of the VERBMOBIL corpus. Training first on a fraction of the data, then retraining on another fraction, both reduces time costs and significantly improves recognition rates. For comparison we show recognition rates of Hidden Markov Models (HMMs) on the same corpus, and provide a promising extrapolation for HMM-LSTM hybrids.
Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, a...
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for aut...
Virtually any modern speech recognition system relies on count-based language models. In this thesis...
Abstract. We apply Long Short-Term Memory (LSTM) recurrent neural networks to a large corpus of unpr...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Abstract. In this paper, we carry out two experiments on the TIMIT speech cor-pus with bidirectional...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
One possible explanation for RNN language models' outsized effectiveness in voice recognition is its...
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designe...
End-to-end speech recognition is the problem of mapping raw audio signal all the way to text. In doi...
Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of da...
Long short term memory (LSTM) networks have been gaining popularity in modeling sequential data such...
In this paper we propose a new technique for robust keyword spot-ting that uses bidirectional Long S...
: In this paper, we review the research work that deal with neural network based speech recognition ...
© 2016 IEEE. In recent years, research on language modeling for speech recognition has increasingly ...
Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, a...
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for aut...
Virtually any modern speech recognition system relies on count-based language models. In this thesis...
Abstract. We apply Long Short-Term Memory (LSTM) recurrent neural networks to a large corpus of unpr...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
Abstract. In this paper, we carry out two experiments on the TIMIT speech cor-pus with bidirectional...
Speaker adaptation of deep neural networks (DNNs) based acoustic models is still a challenging area ...
One possible explanation for RNN language models' outsized effectiveness in voice recognition is its...
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designe...
End-to-end speech recognition is the problem of mapping raw audio signal all the way to text. In doi...
Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of da...
Long short term memory (LSTM) networks have been gaining popularity in modeling sequential data such...
In this paper we propose a new technique for robust keyword spot-ting that uses bidirectional Long S...
: In this paper, we review the research work that deal with neural network based speech recognition ...
© 2016 IEEE. In recent years, research on language modeling for speech recognition has increasingly ...
Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, a...
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for aut...
Virtually any modern speech recognition system relies on count-based language models. In this thesis...