scholarly journals An Analysis of Emotional Speech Recognition for Tamil Language Using Deep Learning Gate Recurrent Unit

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Bennilo Fernandes ◽  
Kasiprasad Mannepalli
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Short Term Memory ◽  
Estimation Method ◽  
Emotional Speech ◽  
New Approach ◽  
Training Time ◽  
Emotional Speech Recognition ◽  
Gated Recurrent Unit

Designing the interaction among human language and a registered emotional database enables us to explore how the system performs and has multiple approaches for emotion detection in patient services. As of now, clustering techniques were primarily used in many prominent areas and in emotional speech recognition, even though it shows best results a new approach to the design is focused on Long Short-Term Memory (LSTM), Bi-Directional LSTM and Gated Recurrent Unit (GRU) as an estimation method for emotional Tamil datasets is available in this paper. A new approach of Deep Hierarchal LSTM/BiLSTM/GRU layer is designed to obtain the best result for long term learning voice dataset. Different combinations of deep learning hierarchal architecture like LSTM & GRU (DHLG), BiLSTM & GRU (DHBG), GRU & LSTM (DHGL), GRU & BiLSTM (DHGB) and dual GRU (DHGG) layer is designed with introduction of dropout layer to overcome the learning problem and gradient vanishing issues in emotional speech recognition. Moreover, to increase the design outcome within each emotional speech signal, various feature extraction combinations are utilized. From the analysis an average classification validity of the proposed DHGB model gives 82.86%, which is slightly higher than other models like DHGL (82.58), DHBG (82%), DHLG (81.14%) and DHGG (80%). Thus, by comparing all the models DHGB gives prominent outcome of 5% more than other four models with minimum training time and low dataset.

scholarly journals Deep Learning for Emotional Speech Recognition

2014 ◽  
pp. 311-320 ◽  
Author(s):  
Máximo E. Sánchez-Gutiérrez ◽  
E. Marcelo Albornoz ◽  
Fabiola Martinez-Licona ◽  
H. Leonardo Rufiner ◽  
John Goddard
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Emotional Speech ◽  
Emotional Speech Recognition

scholarly journals Emotional Speech Recognition using Deep Learning

2020 ◽  
Vol 14 (4) ◽  
pp. 39-55
Author(s):  
Othman O. Khalifa ◽  
M. I. Alhamada ◽  
Aisha H. Abdalla
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Emotional Speech ◽  
Emotional Speech Recognition

Deep learning for emotional speech recognition

2020 ◽  
Author(s):  
M. I. Alhamada ◽  
O. O. Khalifa ◽  
A. H. Abdalla
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Emotional Speech ◽  
Emotional Speech Recognition

Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level

2021 ◽  
Vol 20 (6) ◽  
pp. 1-17
Author(s):  
B. Premjith ◽  
K. P. Soman
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Short Term ◽  
Long Short Term Memory ◽  
Target Languages ◽  
Main Components ◽  
Learning Architectures ◽  
Gated Recurrent Unit ◽  
Character Sequence

Morphological synthesis is one of the main components of Machine Translation (MT) frameworks, especially when any one or both of the source and target languages are morphologically rich. Morphological synthesis is the process of combining two words or two morphemes according to the Sandhi rules of the morphologically rich language. Malayalam and Tamil are two languages in India which are morphologically abundant as well as agglutinative. Morphological synthesis of a word in these two languages is challenging basically because of the following reasons: (1) Abundance in morphology; (2) Complex Sandhi rules; (3) The possibilty in Malayalam to form words by combining words that belong to different syntactic categories (for example, noun and verb); and (4) The construction of a sentence by combining multiple words. We formulated the task of the morphological generation of nouns and verbs of Malayalam and Tamil as a character-to-character sequence tagging problem. In this article, we used deep learning architectures like Recurrent Neural Network (RNN) , Long Short-Term Memory Networks (LSTM) , Gated Recurrent Unit (GRU) , and their stacked and bidirectional versions for the implementation of morphological synthesis at the character level. In addition to that, we investigated the performance of the combination of the aforementioned deep learning architectures and the Conditional Random Field (CRF) in the morphological synthesis of nouns and verbs in Malayalam and Tamil. We observed that the addition of CRF to the Bidirectional LSTM/GRU architecture achieved more than 99% accuracy in the morphological synthesis of Malayalam and Tamil nouns and verbs.

scholarly journals Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Widi Hastomo ◽  
Adhitio Satyo Bayangkari Karno ◽  
Nawang Kalbuana ◽  
Ervina Nisfiani ◽  
Lussiana ETP
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer ◽  
Gated Recurrent Unit ◽  
Blue Chip

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

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