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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 1/2019

08.11.2018

Long short-term memory recurrent neural network architectures for Urdu acoustic modeling

verfasst von: Tehseen Zia, Usman Zahid

Erschienen in: International Journal of Speech Technology | Ausgabe 1/2019

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Abstract

Recurrent neural networks (RNNs) have achieved remarkable improvements in acoustic modeling recently. However, the potential of RNNs have not been utilized for modeling Urdu acoustics. The connectionist temporal classification and attention based RNNs are suffered due to the unavailability of lexicon and computational cost of training, respectively. Therefore, we explored contemporary long short-term memory and gated recurrent neural networks Urdu acoustic modeling. The efficacies of plain, deep, bidirectional and deep-directional network architectures are evaluated empirically. Results indicate that deep-directional has an advantage over the other architectures. A word error rate of 20% was achieved on a hundred words dataset of twenty speakers. It shows 15% improvement over the baseline single-layer LSTMs. It has been observed that two-layer architectures can improve performance over single-layer, however the performance is degraded with further layers. LSTM architectures were compared with gated recurrent unit (GRU) based architectures and it was found that LSTM has an advantage over GRU.
Vorheriger Artikel Indonesian graphemic syllabification using a nearest neighbour classifier and recovery procedure
Nächster Artikel Evaluating noise suppression methods for recovering the Lombard speech from vocal output in an external noise field

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Fußnoten
1
The sequence processing using neural networks is usually performed by operating over a context window at the first layer. We have not considered context window in this section for notational convenience.
 
2
Biases are omitted throughout the paper for simplicity.
 
3
“Center for Language Engineering” [Online]. Available: http://​www.​cle.​org.​pk.
 
4
“Python_speech_features toolkit” [Online]. Available: https://​python-speech-features.​readthedocs.​io/​en/​latest/​.
 
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Metadaten
Titel
Long short-term memory recurrent neural network architectures for Urdu acoustic modeling
verfasst von
Tehseen Zia
Usman Zahid
Publikationsdatum
08.11.2018
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 1/2019
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-018-09573-7

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