Speaker Identification Based Proxy Attendance Detection System

Gupta, Neelesh; Jain, Shikha

doi:10.1109/icsc45622.2019.8938327

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…As defined in section IV, we have created 6 The tables below show the comparison results for all experiments. We have compared speaker recognition system accuracy in terms of True Match Rate (TMR).…”

Section: Experement and Resultsmentioning

confidence: 99%

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Improved Speaker Recognition for Degraded Human Voice using Modified-MFCC and LPC with CNN

Moondra¹,

Chahal²

2023

IJACSA

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Economical speaker recognition solution from degraded human voice signal is still a challenge. This article is covering results of an experiment which targets to improve feature extraction method for effective speaker identification from degraded human audio signal with the help of data science. Every speaker's audio has identical characteristics. Human ears can easily identify these different audio characteristics and classify speaker from speaker's audio. Mel-Frequency Cepstral Coefficient (MFCC) supports to get same intelligence in machine also. MFCC is extensively used for human voice feature extraction. In our experiment we have effectively used MFCC and Linear Predictive Coding (LPC) for better speaker recognition accuracy. MFCC first outlines frames and then finds cepstral coefficient for each frame. MFCC use human audio signal and convert it in numerical value of audio features, which is used to recognize speaker efficiently by Artificial Intelligence (AI) based speaker recognition system. This article covers how effectively audio features can be extracted from degraded human voice signal. In our experiment we have observed improved Equal Error Rate (EER) and True Match Rate (TMR) due to high sampling rate and low frequency range for mel-scale triangular filter. This article also covers pre-emphasis effects on speaker recognition when high background noise comes with audio signal.

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Section: Experement and Resultsmentioning

confidence: 99%

“…Similarly Convolutional Neural Network (CNN) is very popular for speaker recognition process [5]. N. Gupta and S. Jain [6] discussed about Siamese network effects with CNN. A. Chowdhury and A. Ross [7] discussed about CNN with degraded human voice.…”

Section: Introductionmentioning

confidence: 99%

Improved Speaker Recognition for Degraded Human Voice using Modified-MFCC and LPC with CNN

Moondra¹,

Chahal²

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…N. Gupta and S. Jain [21] presented that speaker recognition is possible through Convolutional Neural Network (CNN) based speaker recognition system. Siamese and CIFAR network architecture are used in speaker recognition.…”

Section: Related Workmentioning

confidence: 99%

Speaker Recognition Improvement for Degraded Human Voice using Modified-MFCC with GMM

Moondra¹,

Chahal²

2023

IJACSA

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Speaker's audio is one of the unique identities of the speaker. Nowadays not only humans but machines can also identify humans by their audio. Machines identify different audio properties of the human voice and classify speaker from speaker's audio. Speaker recognition is still challenging with degraded human voice and limited dataset. Speaker can be identified effectively when feature extraction from voice is more accurate. Mel-Frequency Cepstral Coefficient (MFCC) is mostly used method for human voice feature extraction. We are introducing improved feature extraction method for effective speaker recognition from degraded human audio signal. This article presents experiment results of modified MFCC with Gaussian Mixture Model (GMM) on uniquely developed degraded human voice dataset. MFCC uses human audio signal and transforms it into a numerical value of audio characteristics, which is utilized to recognize speaker efficiently with the help of data science model. Experiment uses degraded human voice when high background noise comes with audio signal. Experiment also covers, Sampling Frequency (SF) impacts on human audio when "Signal to Noise Ratio" (SNR) is low (up to 1dB) in overall speaker identification process. With modified MFCC, we have observed improved speaker recognition when speaker voice SNR is upto 1dB due to high SF and low frequency range for mel-scale triangular filter.

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Voice Feature Extraction Method Analysis for Speaker Recognition with Degraded Human Voice

Moondra,

Chahal

2023

2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)

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Speaker Identification Based Proxy Attendance Detection System

Cited by 5 publications

References 6 publications

Improved Speaker Recognition for Degraded Human Voice using Modified-MFCC and LPC with CNN

Improved Speaker Recognition for Degraded Human Voice using Modified-MFCC and LPC with CNN

Speaker Recognition Improvement for Degraded Human Voice using Modified-MFCC with GMM

Voice Feature Extraction Method Analysis for Speaker Recognition with Degraded Human Voice

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