Real Time Context-Independent Phone Recognition Using a Simplified Statistical Training Algorithm
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Auteurs : Othman Lachhab [Maroc] ; Joseph Di Martino [France] ; El Hassan Ibn Elhaj [Maroc] ; Ahmed Hammouch [France]Source :
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Abstract
In this paper we present our own real time speaker-independent continuous phone recognition (Spirit) using Context-Independent Continuous Density HMMs (CI-CDHMMs) modeled by Gaussian Mixtures Models (GMMs). All the parameters of our system are estimated directly from data by using an improved Viterbi alignment process instead of the classical Baum-Welch estimation procedure. Generally, in the literature the Viterbi training algorithm is used as a pretreatment to initialize HMMs models that will be most often re-estimated by using complex re-estimation formula. In order to evaluate and compare the performance of our system with other previous works, we use the TIMIT database. The duration test of our recognition system for each sentence is between 2 seconds (for short sentences) to 12 seconds (for long sentences). We get, by combining the 64 possible phones into 39 phonetic classes, a phone recognition correct rate of 71.06% and an accuracy rate of 65.25%. These results compare favorably with previously published works.
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Affiliations:
- France, Maroc
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
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All the parameters of our system are estimated directly from data by using an improved Viterbi alignment process instead of the classical Baum-Welch estimation procedure. Generally, in the literature the Viterbi training algorithm is used as a pretreatment to initialize HMMs models that will be most often re-estimated by using complex re-estimation formula. In order to evaluate and compare the performance of our system with other previous works, we use the TIMIT database. The duration test of our recognition system for each sentence is between 2 seconds (for short sentences) to 12 seconds (for long sentences). We get, by combining the 64 possible phones into 39 phonetic classes, a phone recognition correct rate of 71.06% and an accuracy rate of 65.25%. These results compare favorably with previously published works.</div></front></TEI><affiliations><list><country><li>France</li><li>Maroc</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="Maroc"><noRegion><name sortKey="Lachhab, Othman" sort="Lachhab, Othman" uniqKey="Lachhab O" first="Othman" last="Lachhab">Othman Lachhab</name></noRegion><name sortKey="Ibn Elhaj, El Hassan" sort="Ibn Elhaj, El Hassan" uniqKey="Ibn Elhaj E" first="El Hassan" last="Ibn Elhaj">El Hassan Ibn Elhaj</name></country><country name="France"><region name="Grand Est"><name sortKey="Di Martino, Joseph" sort="Di Martino, Joseph" uniqKey="Di Martino J" first="Joseph" last="Di Martino">Joseph Di Martino</name></region><name sortKey="Hammouch, Ahmed" sort="Hammouch, Ahmed" uniqKey="Hammouch A" first="Ahmed" last="Hammouch">Ahmed Hammouch</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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