scholarly journals Indian Language Identification using Deep Learning

2020 ◽  
Vol 32 ◽  
pp. 01010
Author(s):  
Shubham Godbole ◽  
Vaishnavi Jadhav ◽  
Gajanan Birajdar
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Visual Representations ◽  
Spoken Language ◽  
Language Identification ◽  
Indian Language ◽  
Regular Method ◽  
Speech Database ◽  
Day By Day

Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated namely Log Spectrogram, Gammatonegram and IIR-CQT Spectrogram for audio samples from the standardized IIIT-H Indic Speech Database. These visual representations depict language specific details and the nature of each language. These spectrograms images were then used as an input to the CNN. Classification accuracy of 98.86% was obtained using the proposed methodology.

Deep learning for spoken language identification: Can we visualize speech signal patterns?

2019 ◽  
Vol 31 (12) ◽  
pp. 8483-8501 ◽  
Author(s):  
Himadri Mukherjee ◽  
Subhankar Ghosh ◽  
Shibaprasad Sen ◽  
Obaidullah Sk Md ◽  
K. C. Santosh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speech Signal ◽  
Spoken Language ◽  
Language Identification

scholarly journals Driving Fatigue Detection from EEG Using a Modified PCANet Method

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yuliang Ma ◽  
Bin Chen ◽  
Rihui Li ◽  
Chushan Wang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Traffic Accidents ◽  
Principal Component ◽  
Learning Model ◽  
Driving Fatigue ◽  
Fatigue Detection ◽  
Deep Learning Model

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

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