2021-04-122016-01-0117426596SCOPUS;2-s2.0-84978042338http://hdl.handle.net/10784/27892We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech. Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions. A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds. Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions. Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it. Finally features related with emotions in voiced speech are extracted and presented.enghttps://v2.sherpa.ac.uk/id/publication/issn/1742-6596info:eu-repo/semantics/conferencePaperBiomedical engineeringFourier analysisFourier transformsPower spectral densitySpectral densitySpectrographsSpeech analysis, Emotion identificationsEmotion recognition from speechGaussian windowQuasi-periodicShort time Fourier transformsSpatial Fourier analysisTime-frequency distributionsVocal tract resonances, Speech recognition2021-04-12Sierra-Sosa, D.Bastidas, M.Ortiz, P.D.Quintero, O.L.10.1088/1742-6596/705/1/012035