Language identification system using MFCC and prosodic features

This paper report the experiments carried out on a recently collected multilingual speech database namely Arunachali Language Speech Database (ALS-DB) to identify the spoken language of the speaker. The speech database consists of speech data recorded from 200 speakers with Arunachali languages of North-East India as mother tongue. The speech data is collected in three different languages English, Hindi and a local language which belongs to any one of the four major languages of Arunachal Pradesh: Adi, Nyishi, Galo and Apatani. The collected database is evaluated with Gaussian mixture model (GMM) based language identification system with MFCC and MFCC with Prosodic features as feature vector. The initial study explores the fact that performance of a baseline GMM-MFCC based language identification system improves considerably when the prosodic features are considered as additional features with MFCC. It has been observed that when prosodic features are combined with MFCC features, performance of the system improved by nearly 11% over the baseline performance.