Review of Automatic Language Identification System in Indian languages from the Non-Uniform Region

Review of Automatic Language Identification System in Indian languages from the Non-Uniform Region

KARTHIKA M R, Prof. ANU GEORGE

Dept. of Electronics and Communication

College of Engineering Trivandrum

Abstract—The process of language identification involves au-tomatically detecting which natural language(s) are given in a speech sample. Using automatic language recognition, an audio clip can be recognised as being spoken in a specific language (LID). Identifying the language correctly from a given speech sample is the language identification system’s fundamental purpose. There are two types of systems they are explicit language identification and implicit language identification. The phoneme sequence used in the explicit language recognition system is produced from a speech sample. Language is determined using the phoneme sequence that is retrieved. There is no need to identify the phoneme in the implicit language recognition method, which determines using certain speech properties. The language identification process involves training and a classifier model. the first step is to extract the speech sample using phonemes then it is trained using classifiers. Data are randomly collected from male and female peoples in society.

Index Terms—Language identification (LID), Mel-frequency cepstral coefficients (MFCC), broad phoneme classifier (BPC),Neural network, support vector machine (SVM), LDA, CNN,i-vector,GMM Super Vector(GSV),Factor Analysis(FA),Native Language Identification (NLI),Stylistically Related Text Samples (SSTs),Deep Dumb Multi Layer Perceptron (DDMLP), Deep Convolutional Neural Network (DCNN), and Semi-supervised Generative Adversarial Network(SSGAN),Gated Recurrent Units (GRUs),Hidden Markov models (HMMs),phone error rate (PER),Convolutional and Long Short Term Memory Recurrent (CLSTM), Neural Networks ,Singing Language Identification (SLID),Automatic Speech Recognition (ASR).