Publication Type : Conference Proceedings
Publisher : 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai.
Source : 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, p.1-4 (2018)
Keywords : Cepstral analysis, Conferences, Feature extraction, formant feature vectors, Formants, government intelligence, Language identification, language identification research area, language translation, LDA, learning (artificial intelligence), Mel frequency cepstral coefficient, Mel frequency cepstral coefficients, MFCC, multilingual translation services, NAtural language processing, Pattern classification, Resonant frequency, Speech, speech based language identification system, Speech features, speech processing, Speech recognition, supervised learning algorithms, Support vector machines, SVM, telephone services, Vectors.
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : Speech based language identification system has a wide range of applications in the field of telephone services, multilingual translation services, government intelligence and monitoring etc. Identifying the exact speech feature for classification is an important problem in the language identification research area. In this work, we are comparing the performance measures of a language identification system using two different supervised learning algorithms. Mel frequency cepstral coefficients and formant feature vectors are extracted for classification purpose. The system which is developed using the database of seven different Indian languages is capable of identifying languages with LDA giving a maximum classification accuracy of 93.88% when compared to SVM with a classification accuracy of 84%.
Cite this Research Publication : J. S. Anjana and Poorna S. S., “Language Identification From Speech Features Using SVM and LDA”, 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). Chennai, pp. 1-4, 2018