Six-layer Optimized Convolutional Neural Network for Lip Language Identification

In Indian Population there is about 1 percent of the people are deaf and dumb. Deaf and dumb people use gestures to interact with each other. Ordinary humans fail to grasp the significance of gestures, which makes interaction between deaf and mute people hard. In attempt for ordinary citizens to understand the signs, an automated sign language identification system is proposed. A smart wearable hand device is designed by attaching different sensors to the gloves to perform the gestures. Each gesture has unique sensor values and those values are collected as an excel data. The characteristics of movements are extracted and categorized with the aid of a convolutional neural network (CNN). The data from the test set is identified by the CNN according to the classification. The objective of this system is to bridge the interaction gap between people who are deaf or hard of hearing and the rest of society.

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Spoken Language Identification with Deep Convolutional Neural Network and Data Augmentation

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302425 ◽

2020 ◽

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Spoken Language ◽

Language Identification ◽

Deep Convolutional Neural Network

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End-to-End Language Identification Using a Residual Convolutional Neural Network with Attentive Temporal Pooling

2019 27th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco.2019.8902777 ◽

2019 ◽

Author(s):

Joao Monteiro ◽

Jahangir Alam ◽

Gautam Bhattacharya ◽

Tiago H. Falk

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Language Identification ◽

End To End

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Automatic Sign Language Identification Using Convolutional Neural Network

Computational Intelligence in Pattern Recognition - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-16-2543-5_25 ◽

2021 ◽

pp. 293-302

Author(s):

Himadri Mukherjee ◽

Ankita Dhar ◽

S. K. Md. Obaidullah ◽

Santanu Phadikar ◽

Kaushik Roy

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sign Language ◽

Language Identification

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Spoken Language Identification Using Deep Learning

Computational Intelligence and Neuroscience ◽

10.1155/2021/5123671 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Gundeep Singh ◽

Sahil Sharma ◽

Vijay Kumar ◽

Manjit Kaur ◽

Mohammed Baz ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Spoken Language ◽

Language Identification ◽

Fixed Duration ◽

Audio Clip ◽

Test Sets ◽

Audio Files

The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian, Tamil, Mandarin, Turkish, Chinese, Arabic, Hindi, Indonesian, Portuguese, Japanese, Latin, Dutch, Portuguese, Pushto, Romanian, Korean, Russian, Swedish, Tamil, Thai, and Urdu. An experiment was conducted on different audio files using the Kaggle dataset named spoken language identification. These audio files are comprised of utterances, each of them spanning over a fixed duration of 10 seconds. The whole dataset is split into training and test sets. Preparatory results give an overall accuracy of 98%. Extensive and accurate testing show an overall accuracy of 88%.

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