Performance Of Language Identification System Research Articles

Performance Enhancement of Indian LID System Using Spectral Processing

Language Identification is a system of recognizing the dialect from a speech sample articulated by an obscure speaker. In this paper, we tend to develop an efficient baseline system for language identification by Mel Frequency Cepstral Coefficients (MFCC) and Gaussian Mixture Model (GMM). The Language Identification (LID) performance is analyzed in speaker independent approach. LID system is developed using IITKGP-MLILSC database. To enhance the LID performance under background noise we use some enhancement techniques like Spectral Subtraction (SS) and Minimum Mean Square Error (MMSE). Here, we present the performance of LID system by varying the amount of training data, the length of testing samples and the background noise.

Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC)

This paper focuses on the task of identifying a language from speech signal. In this paper, we have use Mel-frequency cepstral coefficient as features. Language identification models are developed for fifteen Indian languages namely Assamese, Bangla, Guajarati, Hindi, Kannada, Kashmiri, Malayalam, Marathi, Nepali, Oriya, Punjabi, Rajasthani, Tamil, Telugu and Urdu using these spectral features. The identification of above mentioned languages is carried out using Gaussian mixture model. A Semi natural read database is used for obtaining the language specific information. MFCC is obtained by using linear cosine transform of log power spectrum on a nonlinear mel-frequency scale. This paper shows that the performance of Language identification system is better when trained and tested with twenty nine features as compared to six, eight, thirteen, nineteen and twenty one MFCC features. It means more the number of features we use better the result we get. The average language recognition rate over fifteen Indian languages is around 88\\%.

Improving the Performance of Language Identification System Using Different GMM-Training Approaches

This paper proposed a feasible system for language identification (LID) and designed four different GMM-training approaches to improve the system performance by accuracy recognition rates. In our experiment, we used these model-training approaches to evaluation on the system performance, which utilizes Linear Prediction Cepstrum Coefficients (LPCC) and Gaussian Mixture Model (GMM), rely on a 10-language task. From all the results, we found an optimal approach for training GMM in LID system, which achieves high accuracy of 85.25%, and indicated that different GMM-training approaches have different performances for LID system, but an advisable training method that proposed in our paper can greatly improve the system performance.