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        <title>Speech Recognition for Tigrinya language Using Deep Neural Network Approach</title>
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            <title>Proceedings of the 2019 Workshop on Widening NLP</title>
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            <namePart type="given">Diyi</namePart>
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    <abstract>This work presents a speech recognition model for Tigrinya language .The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks, is the primary layer of our neural network model. The 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN were used. The acoustic models are trained on features that are obtained by projecting down to 40 dimensions using linear discriminant analysis (LDA). Moreover, speaker adaptive training (SAT) is done using a single feature-space maximum likelihood linear regression (FMLLR) transform estimated per speaker. We train and compare LSTM and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models. Finally, the accuracy of the model is evaluated based on the recognition rate.</abstract>
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%0 Conference Proceedings
%T Speech Recognition for Tigrinya language Using Deep Neural Network Approach
%A Abera, Hafte
%A H/mariam, Sebsibe
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F abera-h-mariam-2019-speech
%X This work presents a speech recognition model for Tigrinya language .The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks, is the primary layer of our neural network model. The 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN were used. The acoustic models are trained on features that are obtained by projecting down to 40 dimensions using linear discriminant analysis (LDA). Moreover, speaker adaptive training (SAT) is done using a single feature-space maximum likelihood linear regression (FMLLR) transform estimated per speaker. We train and compare LSTM and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models. Finally, the accuracy of the model is evaluated based on the recognition rate.
%U https://aclanthology.org/W19-3603
%P 7-9

Markdown (Informal)

[Speech Recognition for Tigrinya language Using Deep Neural Network Approach](https://aclanthology.org/W19-3603) (Abera & H/mariam, WiNLP 2019)

• Speech Recognition for Tigrinya language Using Deep Neural Network Approach (Abera & H/mariam, WiNLP 2019)

ACL

• Hafte Abera and Sebsibe H/mariam. 2019. Speech Recognition for Tigrinya language Using Deep Neural Network Approach. In Proceedings of the 2019 Workshop on Widening NLP, pages 7–9, Florence, Italy. Association for Computational Linguistics.