English digits speech recognition system based on Hidden Markov Models

Abushariah, Ahmad A. M.; Gunawan, Teddy Surya; Khalifa, Othman O.; Abushariah, Mohammad A. M.

doi:10.1109/iccce.2010.5556819

Cited by 60 publications

(21 citation statements)

References 2 publications

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“…The parameter to find the accuracy of a recognition system is the Recognition Rate (RR) which is given by (9) where Ncorrect is the number of words recognized correctly and Ntotal is the total number of words in the vocabulary [5] (9) % 100 Rate n Recognitio   Ntotal…”

Section: Resultsmentioning

confidence: 99%

“…He has also introduced the concept of frame wise averaging the coefficients of LPCC, which has slightly increased the accuracy of recognition [This averaging method has been used in this paper.] Similarly, MFCC based feature extraction has been carried out in [5] and Perception Linear Prediction (PLP) and Euclidean distance based speech recognition has been implemented in [6]. Once the features are extracted for a given signal, they have to be compared with the feature of the references stored which depends on the vocabulary of the recognition system.…”

Section: Introductionmentioning

confidence: 99%

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Digit Recognition based on Euclidean and DTW

Nair¹,

Shah²

2015

IJCA

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This paper describes the implementation of two isolated digit recognition techniques and is a comparison between the algorithms implemented. Any digit recognition comprises of mainly two stages feature extraction and similarity evaluation. Here, two feature extraction techniques, namely linear predictive cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are implemented and the similarity evaluation is done using Euclidean distance and Dynamic Time Warping (DTW). In DTW both single and averaged template matching is done. The results obtained for these algorithms are perused, compared and conclusions are drawn.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digit Recognition based on Euclidean and DTW

Nair¹,

Shah²

2015

IJCA

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“…System obtained 88.0% accuracy [15]. [20]. Karpagavalli, Rani, Deepika, & Kokila, (2012) have described isolated tamil digit speech recognition using VQ in which proposed model is based on small vocabulary isolated speaker independent tamil digits.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Survey on Isolated Word and Digit Recognition using Different Techniques

Prajapati¹,

Patel²

2017

IJCA

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“…Speech Recognition Hidden Markov Model [Abushariah et al 2010] uses stochastic model to compare the unknown utterance generated by each model. HMM are described with finite state automata.…”

Section: Traditional Hidden Markov Model Formentioning

confidence: 99%