Experiments on automatic language identification for philippine languages using acoustic Gaussian Mixture Models

A Philippine LID system has not been previously created because of the limited amount of recorded speech data. This research initiates the LID research using the Philippine Language Database (PLD) collected by the Digital Signal Processing Laboratory of the University of the Philippines Diliman (DSP-UPD). Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), Shifted Delta Cepstra (SDC) and Linear Predictive Cepstral Coefficients (LPCC) features are extracted from the speech segments. Gaussian Mixture Model (GMM) using Expectation Maximization (EM) and Universal Background Model (UBM) approach is used to model the acoustic characteristics of the language. Maximum a Posteriori (MAP) probability is then used to determine the language of a speech utterance based on the language GMMs. PLP using a 16 Mixture GMM-EM has been found to produce the best performance among the four feature vectors in discriminating the languages.