Enhanced Feature Set Calculation from Emotional Speech Signals

Dendukuri, Lakshmi Srinivas; Hussain, Shaik Jakeer

doi:10.1109/vitecon.2019.8899416

Cited by 2 publications

(1 citation statement)

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“…It involves the modeling of time varying filters such as vocal tract, mouth and lips to vary from emotion to emotion in production of emotional speech signal. Dendukri and Hussain [18] proposed the extraction of feature set from voiced regions of speech for speech emotion recognition application. Wavelet transform is applied on the speech signal, hard and soft thresholding are applied on detail coefficients for the extraction of voiced speech.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Voiced Regions of Speech from Emotional Speech Signals Using Wavelet-Pitch Method

Dendukuri

Hussain

2021

Period. Polytech. Elec. Eng. Comp. Sci.

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Extraction of voiced regions of speech is one of the latest topics in speech domain for various speech applications. Emotional speech signals contain most of the information in voiced regions of speech. In this particular work, voiced regions of speech are extracted from emotional speech signals using wavelet-pitch method. Daubechies wavelet (Db4) is applied on the speech frames after downsampling the speech signals. Autocorrelation function is performed on the extracted approximation coefficients of each speech frame and corresponding pitch values are obtained. A local threshold is defined on obtained pitch values to extract voiced regions. The threshold values are different for male and female speakers, as male pitch values are low compared to the female pitch values in general. The obtained pitch values are scaled down and are compared with the thresholds to extract the voiced frames. The transition frames between the voiced and unvoiced frames are also extracted if the previous frame is voiced frame, to preserve the emotional content in extracted frames. The extracted frames are reshaped to have desired emotional speech signal. Signal to Noise Ratio (SNR), Normalized Root Mean Square Error (NRMSE) and statistical parameters are used as evaluation metrics. This particular work provides better SNR and Normalized Root Mean Square Error values compared to the zero crossing-energy and residual signal based methods in voiced region extraction. Db4 wavelet provides better results compared to Haar and Db2 wavelets in extracting voiced regions using wavelet-pitch method from emotional speech signals.

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Section: Related Workmentioning

confidence: 99%