Abstract
Detecting human intentions and emotions helps improve human–robot interactions. Emotion recognition has been a challenging research direction in the past decade. This paper proposes an emotion recognition system based on analysis of speech signals. Firstly, we split each speech signal into overlapping frames of the same length. Next, we extract an 88-dimensional vector of audio features including Mel Frequency Cepstral Coefficients (MFCC), pitch, and intensity for each of the respective frames. In parallel, the spectrogram of each frame is generated. In the final preprocessing step, by applying k-means clustering on the extracted features of all frames of each audio signal, we select k most discriminant frames, namely keyframes, to summarize the speech signal. Then, the sequence of the corresponding spectrograms of keyframes is encapsulated in a 3D tensor. These tensors are used to train and test a 3D Convolutional Neural network using a 10-fold cross-validation approach. The proposed 3D CNN has two convolutional layers and one fully connected layer. Experiments are conducted on the Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE’05 databases. The results are superior to the state-of-the-art methods reported in the literature.
Highlights
Designing an accurate automatic emotion recognition (ER) system is crucial and beneficial to the development of many applications such as human–computer interactive (HCI) applications [1], computer-aided diagnosis systems, or deceit-analyzing systems
Taking into account the acquisition source of the data, three general groups of emotional databases exist: spontaneous emotions, acted emotions based on invocation and simulated emotions
Sample databases recorded in natural situations such as TV shows or movies are categorized under the first group
Summary
Designing an accurate automatic emotion recognition (ER) system is crucial and beneficial to the development of many applications such as human–computer interactive (HCI) applications [1], computer-aided diagnosis systems, or deceit-analyzing systems. Three main models are in use for this purpose, namely acoustic, visual, and gestural. Speech emotion recognition (SER) is useful for addressing HCI problems provided that it can overcome challenges such as understanding the true emotional state behind spoken words. In this context, SER can be used to improve human–machine interaction by interpreting human speech. SER refers to the field of extracting semantics from speech signals. Applications such as pain and lie detection, computer-based tutorial systems, and movie or music recommendation systems that rely on the emotional state of the user can benefit from such an automatic system. The main goal of SER is to detect discriminative features of a speaker’s voice in different emotional situations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
