N-gram language model based continuous voiced odia digit recognition

Abstract

With the enormous improvement in the area of signal processing, speech processing systems are creating a massive impact in recognizing the voices, controlling the commands and making as communication interfaces. A continuous speech recognition system is essential for voice identification hands free system used as a voice dialer, voice originated security systems and voice based automatic electronic machines. The proposed work suggests a finest speaker independent continuous voiced digit recognition for Odia language. The model integrates the concept of Mel Frequency Cepstral Coefficient (MFCC) and continuous density Hidden Markov Model (HMM), relating to speech parameterization and recognition respectively. The performance of the model is explored for different levels of HMM like word-level and phoneme-level. Further the model output is evaluated using different N-Gram approaches of the language model. Finally it is shown that the model using phoneme-level HMM with a tri-gram language model is superior to other methodologies.