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Punjabi language is popular Indo-Aryan language. Its phoneme sounds are tonal in nature which dissent in almost all-Indian side of Punjab. This books focus on analysis of some pf the dominant feature extraction techniques used in Automatic Speech Recognition and analytically analyse which feature extraction techniques is best suitable for extracting features of the tone present in the Punjabi speech. Three feature extractions techniques are compared: ¿power normalized cepstral coefficients (PNCC)¿, ¿Mel frequency cepstral coefficients (MFCC)¿ and ¿Perceptual Linear Prediction (PLP)¿ following a statistical comparison based on the accuracy and correctness of results attained. To attain a higher rate of accuracy level 34 phones for Punjabi language are used to break each word into small sound frames. environments using evident number of speakers giving overall system results with MFCC as finest of all three in noise-free environment and PLP to be efficient feature extraction technique in noisy environment for Punjabi speech corpus.
Speech recognition is the hottest form of AI research these days, but many under resources languages still haven't traversed yet. This book deals with one of undewr resource language: Kashmiri language. Authors describe the development as well as implementation of CMU Sphinx-3 based speech recognizer for the Kashmiri language. Recognition of the words has been done by using hidden markov models (HMMs). Dictionary data consists of one (akh) to hundred (hat) Kashmiri digits, most frequent Kashmiri words and sentences from the book ¿Spoken Kashmiri a Language Course¿ by Omkar N. Koul. Here, we developed a speaker independent, Kashmiri - Automatic Speech Recognition (K-ASR) system for continuous speech. System is trained using the speech corpora that contains 47 Kashmiri sentences, a total of 2800 words spoken by 12 male and female speakers in noise as well as in noise-free environmental conditions. Maximum Accuracy of 89.78% was achieved, when the K-ASR system was tested in noise free environment.
This concise book provides a survival toolkit for efficient, large-scale software development.
This book presents an emotion centered research framework titled "emoha" for design innovation. It defines emoha and underlines the importance of the developed framework in culturalization of technology and thereby design innovation.
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