Automatic gender identification by speech signal using eigenfiltering based on Hebbian learning

Fagundes, Rubem Dutra Ribeiro; Martins, Andrew; Castro, Fernando Comparsi de; Castro, Maria Cristina Felippetto de

doi:10.1109/sbrn.2002.1181476

Cited by 2 publications

(6 citation statements)

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“…2. The proposed GR method has a very low computational complexity and therefore consumes a limited quantity of energy, nevertheless it guarantees 100% recognition performance, as the solutions proposed in [10] and [33].…”

Section: ) Proposed Gender Recognition Algorithmmentioning

confidence: 96%

“…Together with the Mel Frequency Cepstral Coefficients (MFCC) [10], pitch is the most frequently used feature [11]- [14] since it is a physiologically distinctive trait of a speaker's gender. Other employed features are formant frequencies and bandwidths, open quotient and source spectral tilt correlates [12], energy between adjacent formants [15], fractal dimension and fractal dimension VOLUME 1, NO.…”

Section: ) Gender Recognition Featuresmentioning

confidence: 99%

“…As reported in papers such as [10], [11], [13], [32], [33] audio-based Gender Recognition (GR) has many applications. For example: gender-dependent model selection for the improvement of automatic speech recognition and speaker identification, content-based multimedia indexing systems, interactive voice response systems, voice synthesis and smart human-computer interaction.…”

Section: A Gender Recognition (Gr) Subsystemmentioning

confidence: 99%

“…Different kinds of classifiers are used to identify the speaker gender starting from features: e.g., Continuous Density Hidden Markov Models [13], [16], Gaussian Mixture Model (GMM) [14], [16], Neural Networks [10], [32], Support Vector Machines [12], [33]. The percentages of correct recognition of the speaker gender are reported in Table 1 for most classifiers referenced above.…”

Section: A Gender Recognition (Gr) Subsystemmentioning

confidence: 99%

“…ρ pitch = F s τ pitch (10) ρ pitch is the reference pitch value in the remainder of this paper. Autocorrelation downsampling may cause the ''Fine Search'' to be applied around a local maximum of the autocorrelation in (3) instead of the global one.…”

Section: Emerging Topics In Computingmentioning

confidence: 99%

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Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications

Bisio

Delfino

Lavagetto

et al. 2013

IEEE Trans. Emerg. Topics Comput.

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This paper proposes a system that allows recognizing a person's emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions (anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition (GR) and 2) emotion recognition (ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speaker's gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.

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Section: ) Proposed Gender Recognition Algorithmmentioning

confidence: 96%