1,663 research outputs found
Automatic Speech Emotion Recognition Using Machine Learning
This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS
Learning Audio Sequence Representations for Acoustic Event Classification
Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a 'hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame-level. In this paper, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and a RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC
Speech emotion recognition via multiple fusion under spatial–temporal parallel network
The authors are grateful to the anonymous reviewers and the editor for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 61702066), the Chongqing Research Program of Basic Research and Frontier Technology, China (No. cstc2021jcyj-msxmX0761) and partially supported by Project PID2020-119478GB-I00 funded by MICINN/AEI/10.13039/501100011033 and by Project A-TIC-434- UGR20 funded by FEDER/Junta de Andalucía Consejería de Transformación Económica, Industria, Conocimiento Universidades.Speech, as a necessary way to express emotions, plays a vital role in human communication. With the continuous deepening of research on emotion recognition in human-computer interaction, speech emotion recognition (SER) has become an essential task to improve the human-computer interaction experience. When performing emotion feature extraction of speech, the method of cutting the speech spectrum will destroy the continuity of speech. Besides, the method of using the cascaded structure without cutting the speech spectrum cannot simultaneously extract speech spectrum information from both temporal and spatial domains. To this end, we propose a spatial-temporal parallel network for speech emotion recognition without cutting the speech spectrum. To further mix the temporal and spatial features, we design a novel fusion method (called multiple fusion) that combines the concatenate fusion and ensemble strategy. Finally, the experimental results on five datasets demonstrate that the proposed method outperforms state-of-the-art methods.National Natural Science Foundation of China 61702066Chongqing Research Program of Basic Research and Frontier Technology, China cstc2021jcyj-msxmX0761MICINN/AEI/10.13039/501100011033: PID2020-119478GB-I00FEDER/Junta de Andalucía A-TIC-434- UGR2
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