Improved Silence-Unvoiced-Voiced (SUV) Segmentation for Dysarthric Speech Signals using Linear Prediction Error Variance

Ijitona, Tolulope; Yue, Hong; Soraghan, John; Lowit, Anja

doi:10.1109/icccs49078.2020.9118462

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Before starting to apply the proposed approach, it is first necessary to pre-process the signals as shown in Figure 1. This involves firstly removing silence, then secondly detecting the beginning and the end of the speech by using the zero-crossing rate (ZCR) [16]- [19]. The third step is making them equal in length by using zero padding [20], [21] because the training dataset can contain several signals that do not have the same length.…”

Section: Research Methods 21 Pre-processingmentioning

confidence: 99%

Comparative study to realize an automatic speaker recognition system

Abakarim

Abenaou

2022

IJECE

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In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.

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Section: Research Methods 21 Pre-processingmentioning

confidence: 99%

Comparative study to realize an automatic speaker recognition system

Abakarim

Abenaou

2022

IJECE

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“…Atal and Rabiner [24] utilized ZCR, STE, the correlation between adjacent speech samples, LPC analysis, and LPC error for the segmentation of voiced and unvoiced speech. Ijitona et al [25] proposed a method based on the combination of linear prediction error variance (LPEV), STE, and ZCR for the segmentation of voiced, unvoiced, and silence regions. In this study, we employed STE to categorize each speech frame into voiced and unvoiced frames.…”

Section: Segmentation Of Voiced and Unvoiced Regionmentioning

confidence: 99%

Significance of voiced and unvoiced speech segments for the detection of common cold

Warule

Mishra

Deb

2022

SIViP

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This work investigates the significance of the voiced and unvoiced region for detecting common cold from the speech signal. In literature, the entire speech signal is processed to detect the common cold and other diseases. This study uses a short-time energy-based approach to segment the voiced and unvoiced region of the speech signal. Then, frame-wise mel frequency cepstral coefficients (MFCC) features are extracted from the voiced and unvoiced segments of each speech utterance, and statistics (mean, variance, skewness, and kurtosis) are calculated to get the feature vector for each speech utterance. The support vector machine (SVM) is utilized to analyze the performance of features extracted from the voiced and unvoiced region. Result shows that the feature extracted from voiced segments, unvoiced segments, and complete active speech (CAS) gives almost similar results using the MFCC features and SVM classifier. Therefore, rather than processing the CAS, we can process the unvoiced speech segments, which have fewer frames compared to CAS and voiced regions of speech. The processing of solely unvoiced segments can reduce the time and computation complexity of a speech signal-based common cold detection system.

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“…Researchers implemented these segmentations by using different machine learning and clustering techniques. In [19], the author presented a novel approach for segmenting dysarthric speech into silent, unvoiced, and voiced pieces. Short-time energy, zerocrossing rate, and linear prediction error variance are used to solve the segmentation issue in this example.…”

Section: Related Workmentioning

confidence: 99%

Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering

Mehrotra

Shukla

Chaudhary

et al. 2022

Mathematical Problems in Engineering

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Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.

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Improved Silence-Unvoiced-Voiced (SUV) Segmentation for Dysarthric Speech Signals using Linear Prediction Error Variance

Cited by 5 publications

References 14 publications

Comparative study to realize an automatic speaker recognition system

Comparative study to realize an automatic speaker recognition system

Significance of voiced and unvoiced speech segments for the detection of common cold

Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering

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