Investigation of Spoken-Language Detection in Multilingual Environment

Abstract

Spoken language contains lot of information such as information about the content of a message and information about the speaker of that message. Content is composed of several levels of linguistic information like phonological information, morphological information, syntactic information, and the semantic information. For Present study, Multilingual Speech Processing database of different speakers has been recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The sentences consist of consonants, i.e., “Cha”, “Sha” and “Jha”. Total numbers of speakers involved are 30 including males and females. The basic features of the speech signal: Pitch and first three Formant F1, F2 and F3 are calculated through PRAAT software whereas cepstral features Mel- Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) has been extracted from MATLAB software. A model is proposed to identify the speaker by multi language speech signal of a speaker using MFCC, GFCC and combine features as acoustic features. For training and testing, is performed on using neural network function Resilient Back Propagation Algorithm and Radial Basis Functions and results are compared. In this experiment accuracy of spoken language identification is 94.77% using BPA and 96.52% using RBF neural network.