Experiments on the MFCC application in speaker recognition using Matlab

Bhattarai, Kritagya; Prasad, P. W. C.; Alsadoon, Abeer; Pham, L.; Elchouemi, A.

doi:10.1109/icist.2017.7926796

Cited by 18 publications

(3 citation statements)

References 6 publications

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“…The features that SHAP found to have an impact on modeling decisions are well supported by previous research. For example, MFCC features have already proven to be useful in a wide range of audio tasks such as speaker recognition [ 48 ], music information retrieval [ 38 ], voice activity detection [ 49 ], and most importantly, in voice quality assessment [ 50 ]. Similarly, the high impact of the HNR and the measures of uncertainty in F0 estimation (RPDE) and inability to maintain a constant F0 (PPE) on the model’s output are in congruence with the findings from Little et al [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Rahman¹,

Lee²,

Islam³

et al. 2021

J Med Internet Res

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Background Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases—fueled mostly by environmental pollution and an aging population—can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. Objective In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. Methods We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, “the quick brown fox jumps over the lazy dog.” We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning–based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model’s output. Results We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost—a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing “ahh”) influence the model’s decision the most. Conclusions Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

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Section: Discussionmentioning

confidence: 99%

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Rahman¹,

Lee²,

Islam³

et al. 2021

J Med Internet Res

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“…The keyword recognition system based on neural network is mainly composed of feature extraction module and keyword classification module. The speech feature extraction module mainly uses Mel Frequency Cepstrum Coefficient (MFCC) [7], [8], Linear Prediction Coefficients (LPC) [13] and other feature extraction methods. A large number of multiplications are common in both MFCC and LPC.…”

Section: Introductionmentioning

confidence: 99%

Background Noise Adaptive Energy-Efficient Keywords Recognition Processor With Reusable DNN and Reconfigurable Architecture

Ding

Zhou

et al. 2022

IEEE Access

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This paper proposes a background noise adaptive energy-efficient keywords recognition processor with Reusable DNN (RDNN) and reconfigurable architecture. To reduce power consumption while maintaining the recognition accuracy of different background noises, the SNR prediction module determines whether the computing mode is low power consumption mode (LPM) or high performance mode (HPM). In LPM, DNN-shift (shift-based deep neural network) is used to achieve high recognition accuracy in a low background noise environment; in HPM, DNN-8bit (8bit weighted deep neural network) is used to achieve low power consumption in a high background noise environment. And the two modes share most of the hardware, and approximate computing is introduced to further reduce power consumption. Evaluated under 22nm process technology, this work can support up to 10 keywords recognition with the power consumption of 11.2µW for high background noise and 7.3µW for low background noise.

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“…Speaker recognition with MFCC application using Matlab is discussed in [14]. The speech signal features are extracted by MFCC.…”

Section: Introductionmentioning

confidence: 99%

Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

Sajeer*,

Rodrigues

2019

IJEAT

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Identification of a person’s voice from the different voices is known as speaker recognition. The speech signals of individuals are selected by means of speaker recognition or identification. In this work, an efficient method for speaker recognition is made by using Discrete Wavelet Transform (DWT) features and Gaussian Mixture Models (GMM) for classification is presented. The input speech signal features are decomposed by DWT into subband coefficients. The DWT subband coefficient features are the input for the classification. Classification is made by GMM classifier at 4, 8, 16 and 32 Gaussian component levels. Results show a better accuracy of 96.18% speaker signals using DWT features and GMM classifier

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Experiments on the MFCC application in speaker recognition using Matlab

Cited by 18 publications

References 6 publications

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Background Noise Adaptive Energy-Efficient Keywords Recognition Processor With Reusable DNN and Reconfigurable Architecture

Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

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