An MFCC-Based Speaker Identification System

Leu, Fang−Yie; Lin, Guan-Liang

doi:10.1109/aina.2017.130

Cited by 28 publications

(11 citation statements)

References 10 publications

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“…Leu and Lin 9 established a phonetic model of the speaker and gave an arrangement of reasonable framework forms. The explanation behind picking python is that it gives numerous fundamental math‐related expansions, which are extensible, easy to use, and simple to actualize on an assortment of stages.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK‐SVM) classification technique for speech recognition in hearing‐impaired listeners

Pavithra

Janakiraman

2019

Concurrency and Computation

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Summary Automatic speech recognition of Tamil Language with Hearing‐Impaired becomes difficult task in recent decades. In order to deal with the challenges with speech perception in hostile listening situations, Noise Reduction (NR) algorithms have been developed with the aim of improving the speech intelligibility (SI), speech quality, and ease of listening. Even though the noises are removed, extraction of correct features from the speech becomes difficult task. The major aim of this work is to introduce a Classification Technique for Speech Recognition in Hearing‐Impaired Listeners. The binary mask along with its binary weights and the Wiener filter with constant weights form the representatives of a hard and a soft‐decision scheme for time‐frequency masking. In the proposed Log Frequency Power Coefficients (LFPC), Pitch, Mel‐Frequency Cepstral Coefficients (MFCCs), Energy, formants, and intensity as input feature vectors are extracted from preprocessed signals. Then, for automatic speech recognition process, Enhanced Polynomial Kernel (EPK)–based Support Vector Machine (SVM) (EPK‐SVM) classifier is proposed for Hearing Impaired in Tamil language is implemented in MATLAB software. The results obtained showed that SVM is found to be potential in hearing‐impaired application and is validated via the use of the recognition accuracy and error rate.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK‐SVM) classification technique for speech recognition in hearing‐impaired listeners

Pavithra

Janakiraman

2019

Concurrency and Computation

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“…[14,16]. In our work, we focus on the evaluation of voice disorders in the cepstral domain by using Mel Frequency Cepstral Coefficients (MFCC), which have been used for the first time by [17], as well as its uses in various applications, this MFCC is used for the recognition of Alzheimer's disease [18,19], and they have been used extensively in language and speaker identifications [20,23], they have also used for emotion recognition [24,26]. In this study, we sought to distinguish two categories of patients; 20 patients with Parkinson's disease and 18 healthy patients; each person was asked to pronounce the sustained vowel /a/.…”

Section: Introductionmentioning

confidence: 99%

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

et al. 2022

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Parkinson’s disease (PD) is one of the neurodegenerative diseases. The neuronal loss caused by this disease leads to symptoms such as lack of initiative, depressive states, psychological disorders, and impairment of cognitive functions as well as voice dysfunctions. This paper aims to propose a system of automatic recognition of Parkinson’s disease by voice analysis. In this system, we are based on a database of 38 recordings, 20 people with Parkinson’s disease and 18 healthy people pronounce the vowel /a/.at first, we have decomposed the vocal signal of each patient by the Empirical Mode Decomposition (EMD), then, we extract from 1 to 12 coefficients of the Mel Frequency Cepstral Coefficients (MFCC), to obtain the voiceprint from each voice sample, we compressed the frames by computing their average value. At the end of the classification, we have used the validation scheme “holdout” as well as the K-nearest neighbor (KNN) classifier, the performance of this classification gives accuracy up to 86,67% when applied to 80% of the database as training data.

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“…The extraction method is a factor that affects the accuracy value. The mel frequency cepstral coefficients (MFCC) method is a reliable method with high accuracy for high-quality audio recordings [13]. The accuracy rate is more than 90% [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…(n) = 0.54 -0.46 cos (2𝜋n/(N-1)) (12) x(n)=xl*w(n) (13) each frame after windowing is calculated the frequency value, using discrete Fourier transform (DFT) (converting from time domain to frequency). A total of N DFT data is calculated using FFT, to determine the frequency value, ( 14) is used.…”

mentioning

confidence: 99%

Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

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This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dual-channel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data uses a noise variance of up to -16 dB. Testing on the MFCC single-channel method for -16 dB noise has an accuracy of 47.5%, while the MFCC dual-channel method has an accuracy better of 76.25%. The next test used adaptive noise-canceling (ANC) to reduce noise before extraction. The result is that the MFCC single-channel method has an accuracy of 82.5% and the MFCC dual-channel method has an accuracy better of 83.75%. High-quality audio recording testing for the MFCC single-channel method has an accuracy of 92.5% and the MFCC dual-channel method has an accuracy better of 97.5%. The test results show the effect of mel scale bandwidth to increase accuracy. The MFCC dual-channel method has higher accuracy.

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An MFCC-Based Speaker Identification System

Cited by 28 publications

References 10 publications

Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK‐SVM) classification technique for speech recognition in hearing‐impaired listeners

Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK‐SVM) classification technique for speech recognition in hearing‐impaired listeners

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

Feature extraction with mel scale separation method on noise audio recordings

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