Comparison of Deep Learning Methods for Spoken Language Identification

2020 ◽  
pp. 223-231
Author(s):  
Can Korkut ◽  
Ali Haznedaroglu ◽  
Levent Arslan
Keyword(s):  
Deep Learning ◽  
Spoken Language ◽  
Language Identification ◽  
Learning Methods

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

Comparative Study on Spoken Language Identification Based on Deep Learning

2018 ◽  
Author(s):  
Panikos Heracleous ◽  
Kohichi Takai ◽  
Keiji Yasuda ◽  
Yasser Mohammad ◽  
Akio Yoneyama
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Spoken Language ◽  
Language Identification

scholarly journals Spoken Language Identification Using Deep Learning

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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