Isolated Tamil Digit Speech Recognition Using Template-Based and HMM-Based Approaches

Karpagavalli, S.; Deepika, R.; Kokila, Preethi H.; Rani, K. Usha; Chandra, E.

doi:10.1007/978-3-642-29216-3_48

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…MFCC was used for features extraction and LindeBuzo Gray (LBG) victor quantization algorithm was used for code book generation for each digit. The recognition accuracy was 91 % (Karpagavalli et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Ali

Abbas

Thasleema

et al. 2015

Int J Speech Technol

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Automatic recognition of isolated spoken digits is one of the most challenging tasks in the area of Automatic Speech Recognition. In this paper, Database Development and Automatic Speech Recognition of Isolated Pashto Spoken Digits from Sefer (0) to Naha (9) has been presented. A number of 50 individual Pashto native speakers (25 male and 25 female) of different ages, ranging from 18 to 60 years, were involved to utter from Sefer (0) to Naha (9) digits separately. Sony PCM-M 10 linear recorder is used for recoding purpose in the office and home in noise free environment. Adobe audition version 1.0 is used to split the audio of digits into individual digits and result is saved in .wav format. Mel frequency cepstral coefficients is used to extract speech features. K nearest neighbor classifier is used for the first time up to author knowledge in Pashto language to classify the features of speech and compare its accuracy with linear discriminate analysis. The experimental results are evaluated, and the overall average recognition exactitude of 76.8 % is obtained.

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Section: Introductionmentioning

confidence: 99%

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Ali

Abbas

Thasleema

et al. 2015

Int J Speech Technol

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“…× 100 (17) The "connected component" Speech consist of two or more words which are segmented. The first stage in speech recognition is de-noising a speech signal using hard thresholding, soft thresholding and combined thresholding methods.…”

Section: Resultsmentioning

confidence: 99%

“…They improved the recognition accuracy at each phase and finally 87.1% accuracy was obtained. In the paper [17], authors developed speaker independent isolated Tamil digits recognition used and achieved overall recognition accuracy of 91.8%. From input speech signals MFCC feature vectors are extracted and trained using Vector Quantization (VQ) approach.…”

Section: Related Work In Tamil Speech Recognitionmentioning

confidence: 99%

Speech Query Recognition for Tamil Language Using Wavelet and Wavelet Packets

Iswarya¹,

Radha²

2015

J Inf Process Syst

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Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

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“…Here in proposed system, codebook for each word in the vocabulary is done using Linde-Budo-Gray (LBG) vector quantization algorithm.Each digit is evaluated using word error rate & word recognition rate. Which achieves overall 91.8 % accuracy [50].…”

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

confidence: 94%

A Survey on Isolated Word and Digit Recognition using Different Techniques

Prajapati¹,

Patel²

2017

IJCA

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Isolated Tamil Digit Speech Recognition Using Template-Based and HMM-Based Approaches

Cited by 5 publications

References 3 publications

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Database development and automatic speech recognition of isolated Pashto spoken digits using MFCC and K-NN

Speech Query Recognition for Tamil Language Using Wavelet and Wavelet Packets

A Survey on Isolated Word and Digit Recognition using Different Techniques

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