<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becoming popular in automatic speech recognition tasks which combines a good acoustic with a language model. Standard feedforward neural networks cannot handle speech data well since they do not have a way to feed information from a later layer back to an earlier layer. Thus, Recurrent Neural Networks (RNNs) have been introduced to take temporal dependencies into account. However, the shortcoming of RNNs is that long-term dependencies due to the vanishing/exploding gradient problem cannot be handled. Therefore, Long Short-Term Memory (LSTM) networks were introduced, which are a special case of RNNs, that takes long-term dependencies in a speech in addition to short-term dependencies into account. Similarily, GRU (Gated Recurrent Unit) networks are an improvement of LSTM networks also taking long-term dependencies into consideration. Thus, in this paper, we evaluate RNN, LSTM, and GRU to compare their performances on a reduced TED-LIUM speech data set. The results show that LSTM achieves the best word error rates, however, the GRU optimization is faster while achieving word error rates close to LSTM.</p></abstract>

Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becoming popular in automatic speech recognition tasks which combines a good acoustic with a language model. Standard feedforward neural networks cannot handle speech data well since they do not have a way to feed information from a later layer back to an earlier layer. Thus, Recurrent Neural Networks (RNNs) have been introduced to take temporal dependencies into account. However, the shortcoming of RNNs is that long-term dependencies due to the vanishing/exploding gradient problem cannot be handled. Therefore, Long Short-Term Memory (LSTM) networks were introduced, which are a special case of RNNs, that takes long-term dependencies in a speech in addition to short-term dependencies into account. Similarily, GRU (Gated Recurrent Unit) networks are an improvement of LSTM networks also taking long-term dependencies into consideration. Thus, in this paper, we evaluate RNN, LSTM, and GRU to compare their performances on a reduced TED-LIUM speech data set. The results show that LSTM achieves the best word error rates, however, the GRU optimization is faster while achieving word error rates close to LSTM.

Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becoming popular in automatic speech recognition tasks which...

Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

Data publikacji: 30 sie 2019

Zakres stron: 235 - 245

Otrzymano: 29 wrz 2018

Przyjęty: 10 mar 2019

DOI: https://doi.org/10.2478/jaiscr-2019-0006

Słowa kluczowe
Spectrogram, Connectionist Temporal Classification, TED-LIUM data set

© 2019 Apeksha Shewalkar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

Data publikacji: 30 sie 2019

Zakres stron: 235 - 245

Otrzymano: 29 wrz 2018

Przyjęty: 10 mar 2019

DOI: https://doi.org/10.2478/jaiscr-2019-0006

Słowa kluczoweSpectrogram, Connectionist Temporal Classification, TED-LIUM data set

© 2019 Apeksha Shewalkar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Spectrogram, Connectionist Temporal Classification, TED-LIUM data set