Enhancing HMM Based Malayalam Continuous Speech Recognizer Using Artificial Neural Networks

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

Improving discrimination in recognition systems is a subject of research in recent years. Neural network classifiers are naturally discriminative and can be easily applied to real-world problems. This paper examines the use of multilayer perceptrons as the emission probability estimator in a hidden Markov model based continuous speech recognizer for Malayalam language. The performance of the system has been compared with a recognizer using Gaussian mixture model as the emission probability estimator. Experimental results show that the proposed neural network based acoustic scoring yields significant gains in recognition accuracy and system compactness.

Keywords

Continuous speech recognition Malayalam speech recognition Hidden Markov model Gaussian mixture model Artificial neural network 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.School of Computer SciencesMahatma Gandhi UniversityKottayamIndia
  2. 2.Department of Computer Science and EngineeringViswajyothi College of Engineering and TechnologyVazhakulam, MuvattupuzhaIndia

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