Double Fourier analysis for Emotion Identification in Voiced Speech

We 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

• Autores: Sierra-Sosa, D; Bastidas, M; Ortiz P., D.; Quintero, O.L.; Sierra-Sosa, D; Bastidas, M; Ortiz P., D.; Quintero, O.L.
• Fecha de publicación: 2016
• Institución: Universidad EAFIT
• Repositorio: Repositorio EAFIT
• Idioma: eng
• OAI Identifier: oai:repository.eafit.edu.co:10784/8375
• Acceso en línea: http://hdl.handle.net/10784/8375
• Palabra clave: Transformadas de Wavelet; Procesamiento digital de voz; Morfología matemática; ANÁLISIS ESPECTRAL; ANÁLISIS DE FOURIER; PROCESAMIENTO DE SEÑALES; SISTEMAS DE PROCESAMIENTO DE LA VOZ; TRANSFORMACIONES (MATEMÁTICAS); PRINCIPIO DE INCERTIDUMBRE DE HEISENBERG; Spectrum analysis; Fourier analysis; Signal processing; Speech processing systems; Transformations (mathematics); Heisenberg uncertainty principle; Spectrum analysis; Fourier analysis; Signal processing; Speech processing systems; Transformations (mathematics); Heisenberg uncertainty principle; Transformadas de Wavelet; Procesamiento digital de voz; Morfología matemática
• License: Acceso abierto