Analysis of stressed human speech

Mohanty, Mihir Narayan; Jena, Bhagyalaxmi

doi:10.1504/ijcvr.2011.042273

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…It allows the decomposition of discrete time signals into sinusoidal components whose frequencies are multiples of a fundamental frequency [4][5]. The amplitudes and phases of the sinusoidal components can be estimated using the FFT and is represented mathematically as For a given bio-signal ( ) x n whose sampling period is T with N number of total samples (NT is therefore the total duration of the signal segment).…”

Section: A Fast Fourier Transformmentioning

confidence: 99%

“…And it is an FFT algorithm that computes the discrete Fourier transform (DFT) of arbitrary sizes (including prime sizes) by re-expressing the DFT as a convolution. Bluestein's algorithm can also be used to compute a more general transform based on the (unilateral) Z-Transform [4][5][6]. In particular, it can compute any transform in the form: …”

Section: B Chirp Z Transformmentioning

confidence: 99%

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Spectral characterization of brain signal under silent state

Biswal

Behera

Mohanty

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Signal Processing has a major role in most of the research areas. It has exploited well in biological and biomedicalfields since some decades. Biomedical signal processing has been occupied an important space in current research. In this work, a case of unconsciousness condition has been analysed. Authors have worked for brain signal analysis and characterize using spectral methods. The silence condition along with unconscious state has been analyzed in this work. The spectral analysis methods as FFT, CZT and WT are used to characterize for different states. The spectral change result are clearly indicated in this three different states.For this method,the EEG signal has been acquired from normal, alcoholic and epileptic subjects.

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Section: A Fast Fourier Transformmentioning

confidence: 99%

Section: B Chirp Z Transformmentioning

confidence: 99%

Spectral characterization of brain signal under silent state

Biswal

Behera

Mohanty

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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“…For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. However, some emotion recognition systems are based on turn-wise processing [8][9][10].…”

Section: Related Literaturementioning

confidence: 99%

Design of Neural Network Model for Emotional Speech Recognition

Palo

Mohanty

Chandra³

2014

Advances in Intelligent Systems and Computing

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Human-computer interaction (HCI) needs to be improved for the field of recognition and detection. Exclusively, the emotion recognition has major impact on social, engineering, and medical science applications. This paper presents an approach for emotion recognition of emotional speech based on neural network. Linear predictive coefficients and radial basis function network are used as features and classification techniques, respectively, for emotion recognition. Results reveal that the approach is effective in recognition of human speech emotions. Speech utterances are directly extracted from audio channel including background noise. Totally, 75 utterances from 05 speakers were collected based on five emotion categories. Fifteen utterances have been considered for training and rest are for test.The proposed approach has been tested and verified for newly developed dataset.

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Verbal sentiment analysis and detection using recurrent neural network

Mohanty

2022

Advanced Data Mining Tools and Methods for Social Computing

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Analysis of stressed human speech

Cited by 8 publications

References 9 publications

Spectral characterization of brain signal under silent state

Spectral characterization of brain signal under silent state

Design of Neural Network Model for Emotional Speech Recognition

Verbal sentiment analysis and detection using recurrent neural network

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