Development of HMM Based Automatic Speech Recognition System for Indian English
Citations Over Time
Abstract
Automatic speech recognition system converts recorded audio speech signal into text output. Speech recognition has variety of applications in various domains. Hidden Markov Model (HMM) is widely used statistical approach in speech recognition system. The proposed work represents a speaker independent continuous speech recognition system for Indian English speakers using Hidden Markov Model Toolkit (HTK). Mel frequency cepstral coefficients (MFCC) are used as a feature vector. The results for automatic speech recognition system using HTK in different experiments are presented. These three different experiments includes cross-validation mode, without adapting HMMs and with adaptation of HMMs. Also the comparison in the accuracy of the recognized speech is discussed.
Related Papers
- → Infant cry recognition based on feature extraction(2010)3 cited
- → Pitch-based cepstral features for gender classification in noisy environments(2013)1 cited
- → The classification of emotion based on human voice by using Mel Frequency Cepstrum Coefficient (MFCC) and Naive Bayes method(2023)1 cited
- A Robust Mel-frequency Cepstrum Coefficients(2008)
- Application of Biomimetic Technology to Feature Extraction from Acoustic Objects(2014)