Abstract
Humans are the most authoritative language identifiers in the province of speech recognition. They can determine within a glimpse of second whether the language of the hearing speech is known to them or not. This eventuality has come true because of the basic discrimination in the sound pattern characteristics based on frequency, time and perceptual domain. This certitude has provoked the motivation to introduce a potent scheme for the proposed plan. The proposed work includes the identification of three well-spoken languages in India that is English, Hindi and Bengali. The scheme has been encountered using some well known perceptual feature such as pitch along with some physical features like zero-crossing rate (ZCR) of the audio signal. To generate the feature set more efficient, the proposed effort has adopted mel-frequency cepstral coefficients (MFCCs) and the statistical textural features by calculating co-occurrence matrix from MFCC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Garg, Archana, Vishal Gupta, and Manish Jindal. “A survey of language identification techniques and applications.” Journal of Emerging Technologies in Web Intelligence 6.4 (2014): 388–400.
https://www.babbel.com/en/magazine/the-10-most-spoken-languages-in-the-world.
Karpagavalli, S., and E. Chandra. “A Review on Automatic Speech Recognition Architecture and Approaches.” International Journal of Signal Processing, Image Processing and Pattern Recognition 9.4 (2016): 393–404.
Grothe, Lena, Ernesto William De Luca, and Andreas Nürnberger. “A Comparative Study on Language Identification Methods.” LREC. 2008.
Behravan, Hamid, Tomi Kinnunen, and Ville Hautamäki. “Out-of-Set i-Vector Selection for Open-set Language Identification.” Odyssey 2016 (2016): 303–310.
Vatanen, Tommi, Jaakko J. Väyrynen, and Sami Virpioja. “Language Identification of Short Text Segments with N-gram Models.” LREC. 2010.
Torres-Carrasquillo, Pedro A., Douglas A. Reynolds, and John R. Deller. “Language identification using Gaussian mixture model tokenization.” Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on. Vol. 1. IEEE, 2002.
Lakhani, Vrishabh Ajay, and Rohan Mahadev. “Multi-Language Identification Using Convolutional Recurrent Neural Network.” arXiv preprint arXiv:1611.04010(2016).
Dehak, Najim, et al. “Language Recognition via i-vectors and Dimensionality Reduction.” Interspeech. 2011.
Yu, Chengzhu, et al. “UTD-CRSS system for the NIST 2015 language recognition i-vector machine learning challenge.” Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016.
Gwon, Youngjune L., et al. “Language recognition via sparse coding.” INTERSPEECH. 2016.
Chandrasekhar, Vijay, Mehmet Emre Sargin, and David A. Ross. “Automatic language identification in music videos with low level audio and visual features.” Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. IEEE, 2011.
Itrat, Madiha, et al. “Automatic Language Identification for Languages of Pakistan.” International Journal of Computer Science and Network Security (IJCSNS) 17.2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yasmin, G., DasGupta, I., Das, A.K. (2019). Language Discrimination from Speech Signal Using Perceptual and Physical Features. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_32
Download citation
DOI: https://doi.org/10.1007/978-981-10-8055-5_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8054-8
Online ISBN: 978-981-10-8055-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)