Emotion Recognition Using Rational Dilation Wavelet Transform For Speech Signal

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

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Speech Emotional Features Extraction Based on Electroglottograph

Neural Computation ◽

10.1162/neco_a_00523 ◽

2013 ◽

Vol 25 (12) ◽

pp. 3294-3317 ◽

Cited By ~ 7

Author(s):

Lijiang Chen ◽

Xia Mao ◽

Pengfei Wei ◽

Angelo Compare

Keyword(s):

Emotion Recognition ◽

Speech Signal ◽

Vocal Tract ◽

Vocal Folds ◽

Distribution Coefficients ◽

Speech Emotion Recognition ◽

Support Vector ◽

Power Law Distribution ◽

Transform Coefficients ◽

Better Than

This study proposes two classes of speech emotional features extracted from electroglottography (EGG) and speech signal. The power-law distribution coefficients (PLDC) of voiced segments duration, pitch rise duration, and pitch down duration are obtained to reflect the information of vocal folds excitation. The real discrete cosine transform coefficients of the normalized spectrum of EGG and speech signal are calculated to reflect the information of vocal tract modulation. Two experiments are carried out. One is of proposed features and traditional features based on sequential forward floating search and sequential backward floating search. The other is the comparative emotion recognition based on support vector machine. The results show that proposed features are better than those commonly used in the case of speaker-independent and content-independent speech emotion recognition.

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A DFC taxonomy of Speech emotion recognition based on convolutional neural network from speech signal

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) ◽

10.1109/citisia50690.2020.9371841 ◽

2020 ◽

Author(s):

Surendra Malla ◽

Abeer Alsadoon ◽

Simi Kamini Bajaj

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Speech Signal ◽

Speech Emotion Recognition

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A Hybrid Approach to Gender Classification using Speech Signal

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset196110 ◽

2019 ◽

pp. 17-24

Author(s):

M. Yasin Pir ◽

Mohamad Idris Wani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wavelet Transform ◽

Speech Signal ◽

Hybrid Approach ◽

Gender Classification ◽

Power Spectral ◽

Reconstructed Signal ◽

Feed Forward Network ◽

Artificial Neural

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

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Detection of hypernasality from speech signal using group delay and wavelet transform

2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke.2016.7802138 ◽

2016 ◽

Cited By ~ 2

Author(s):

Atefeh Mirzaei ◽

Mansour Vali

Keyword(s):

Wavelet Transform ◽

Speech Signal ◽

Group Delay

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Open Quotient Measurements Based on Multiscale Product of Speech Signal Wavelet Transform

Research Letters in Signal Processing ◽

10.1155/2007/62521 ◽

2007 ◽

Vol 2007 ◽

pp. 1-5 ◽

Cited By ~ 15

Author(s):

Aïcha Bouzid ◽

Noureddine Ellouze

Keyword(s):

Wavelet Transform ◽

Relative Error ◽

Speech Processing ◽

Speech Signal ◽

Accurate Estimation ◽

Pitch Period ◽

Open Time ◽

Wide Range ◽

Voiced Speech ◽

Egg Signal

This paper describes a multiscale product method (MPM) for open quotient measure in voiced speech. The method is based on determining the glottal closing and opening instants. The proposed approach consists of making the products of wavelet transform of speech signal at different scales in order to enhance the edge detection and parameter estimation. We show that the proposed method is effective and robust for detecting speech singularity. Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important in a wide range of speech processing tasks. In this paper, accurate estimation of GCIs and GOIs is used to measure the local open quotient (Oq) which is the ratio of the open time by the pitch period. Multiscale product operates automatically on speech signal; the reference electroglottogram (EGG) signal is used for performance evaluation. The ratio of good GCI detection is 95.5% and that of GOI is 76%. The pitch period relative error is 2.6% and the open phase relative error is 5.6%. The relative error measured on open quotient reaches 3% for the whole Keele database.

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