LPC and MFCC Performance Evaluation with Artificial Neural Network for Spoken Language Identification

Automatic language identification plays an essential role in wide range of multi-lingual services. Automatic translators to certain language or routing an incoming telephone call to a human switchboard operator fluent in the corresponding language are examples of these applications that require automatic language identification. This paper investigates the usage of Linear Predictive Coding (LPC) and/or Mel Frequency Cepstral Coefficients (MFCC) with Artificial Neural Network (ANN) for automatic language identification. Different orders for the LPC and MFCC have been tested. In addition, different hidden layers, different neurons in every hidden layers and different transfer functions have been tested in the ANN. Three languages; Arabic, English and French have been used in this paper to evaluate the performance of the automatic language identification systems.

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