1,267 research outputs found
Learning spectro-temporal features with 3D CNNs for speech emotion recognition
In this paper, we propose to use deep 3-dimensional convolutional networks
(3D CNNs) in order to address the challenge of modelling spectro-temporal
dynamics for speech emotion recognition (SER). Compared to a hybrid of
Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our
proposed 3D CNNs simultaneously extract short-term and long-term spectral
features with a moderate number of parameters. We evaluated our proposed and
other state-of-the-art methods in a speaker-independent manner using aggregated
corpora that give a large and diverse set of speakers. We found that 1) shallow
temporal and moderately deep spectral kernels of a homogeneous architecture are
optimal for the task; and 2) our 3D CNNs are more effective for
spectro-temporal feature learning compared to other methods. Finally, we
visualised the feature space obtained with our proposed method using
t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct
clusters of emotions.Comment: ACII, 2017, San Antoni
Robust ASR using Support Vector Machines
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units.
In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad
Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema
In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011
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