Speaker Independent Urdu Speech Recognition Using HMM

Ashraf, Javed; Iqbal, Naveed; Khattak, Naveed Sarfraz; Zaidi, Ather Mohsin

doi:10.1007/978-3-642-13881-2_14

Cited by 30 publications

(21 citation statements)

References 5 publications

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“…The work in [8] presents another Urdu speech recognition system using the Sphinx toolkit, which is speaker independent but limited to a 52 isolated-word vocabulary. Urdu uses Arabic script for orthography, and [9] presents a speaker independent Arabic digit recognizer developed using the Sphinx toolkit, with emphasis on using an entirely Arabic environment (as opposed to previous systems using Romanized scripts) within Sphinx.…”

Section: Urdu Speech Recognition Researchmentioning

confidence: 99%

Large vocabulary continuous speech recognition for Urdu

Sarfraz

Hussain

Bokhari

et al. 2010

Proceedings of the 8th International Conference on Frontiers of Information Technology

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This paper presents the development of acoustic and language models for robust Urdu speech recognition using the CMU Sphinx Open Source Toolkit for speech recognition. Three models have been developed incrementally, with the addition of speech data of up to two speakers per pass; one model using data from 40 female speakers only, one from 41 male speakers only, and one with both male and female speakers (81 speakers). This paper presents the current recognition results, and discusses approaches for improving these recognition rates.

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Section: Urdu Speech Recognition Researchmentioning

confidence: 99%

Large vocabulary continuous speech recognition for Urdu

Sarfraz

Hussain

Bokhari

et al. 2010

Proceedings of the 8th International Conference on Frontiers of Information Technology

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“…Acoustic model refers to the process of mapping between speech and statistical representations (HMMs) [11] that are generated against different features extracted using MFCC [9] . Acoustic model uses Baum-Welch or forward-backward training algorithm to create HMMs for each phone.…”

Section: Acoustic Modelmentioning

confidence: 99%

An experimental framework of speaker independent speech recognition system for Kashmiri language (K-ASR) system using Sphinx

Bhardwaj

Kadyan²,

Singh³

et al. 2017

ijetst

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Abstract. Speech to text conversion in various languages have been performed so far but no process has defined for the Kashmiri language. There has been no research done on Kashmiri speech recognition. So in this work, we describe the development as well as implementation of first CMU Sphinx-3 based speech recognizer for the Kashmiri language. Recognition of the words have been done by using hidden markov models (HMMs). Dictionary consists of 100 words, representing Kashmiri digits from one (akh) to hundred (hat).Here, we developed a speaker independent, Kashmiri -Automatic Speech Recognition (K-ASR) system. The System is trained and tested for 1200 words spoken by 12 male and female speakers. Maximum Accuracy of 78.33% was achieved by the K-ASR system.

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“…The database of words is selected from the most frequently used Urdu words. A system using an open source speech recognition framework called Sphinx4, has been presented in [42] for speech recognition. The speech recognition targets Urdu language with a vocabulary of 52 isolated words.…”

Section: Related Workmentioning

confidence: 99%