Title :
Distinctive Phonetic Features (DPFs)-Based Isolated Word Recognition Using Multilayer Neural Networks
Author :
Huda, Mohammad Nurul ; Hasan, Mohammad Mahedi ; Ahmed, Sumon ; Rahman, Dewan Fayzur ; Muhammad, Ghulam ; Kotwal, Mohammed Rokibul Alam ; Banik, Manoj ; Hossain, Md Shahadat
Author_Institution :
Dept. of CSE, United Int. Univ., Dhaka, Bangladesh
Abstract :
This paper describes an isolated word recognition method based on distinctive phonetic features (DPFs). The method comprises two multilayer neural networks (MLNs). The first MLN, MLNLF-DPF, maps local features (LFs) of an input speech signal into discrete DPFs and the second MLN, MLNDyn, restricts dynamics of outputted DPFs by the MLNLF-DPF. In the experiments on Tohokudai Isolated Spoken-Word Database in clean acoustic environment, the proposed recognizer was found to provide a higher word correct rate (WCR) as well as word accuracy (WA) with fewer mixture components in hidden Markov models (HMMs) in comparison with the method proposed by T. Fukuda, et al.
Keywords :
acoustic signal processing; audio databases; hidden Markov models; natural language processing; neural nets; speech recognition; text analysis; Tohokudai isolated spoken-word database; clean acoustic environment; discrete DPF; distinctive phonetic features; hidden Markov model; isolated word recognition; local features; multilayer neural network; speech signal; word accuracy; word correct rate; Accuracy; Acoustics; Artificial neural networks; Electronic mail; Feature extraction; Hidden Markov models; Speech; distinctive phonetic features; hidden markov models; local features; multilayer neural networks;
Conference_Titel :
Integrated Intelligent Computing (ICIIC), 2010 First International Conference on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4244-7963-4
Electronic_ISBN :
978-0-7695-4152-5
DOI :
10.1109/ICIIC.2010.9