Speech and audio signal processing: processing and perception of speech and music [Book Review]

A survey of emotion recognition methods with emphasis on E-Learning environments

Due to the rapid development of human computer interaction systems, the recognition of emotion becomes a challenging task. Various handheld devices such as smart phones and PCs are utilized to recognize the human emotion from the speech. But, the recognition of emotion is burdensome to the human computer interaction system since it differs according to the speaker. To resolve this problem, the Adaptive Fractional Deep Belief Network (AFDBN) is proposed in this paper. Initially, the spectral features are extracted from the input speech signal. The features obtained are the tonal power ratio, spectral flux, pitch chroma and MFCC. The extracted feature set is then given into the network for the classification. Thus, the AFDBN is newly designed by the fractional theory and Deep belief network. Then, the proposed AFDBN method is used to find out the optimal weights which are used to recognize the emotion efficiently. Finally, the experimental results are evaluated and its performance is analyzed by the evaluation metrics which is compared with the existing systems. The outcome of the proposed method attains 99.17% accuracy for Berlin database and 97.74% for Telugu database.

A novel Adaptive Fractional Deep Belief Networks for speaker emotion recognition

The problem of recognition and classification of emotions in speech is one of the most prominent research topics, that has gained popularity, in human-computer interaction in the last decades. Having recognized the feelings or emotions in human conversations might have a deep impact on understanding a human’s physical and psychological situation. This study proposes a novel hybrid architecture based on acoustic and deep features to increase the classification accuracy in the problem of speech emotion recognition. The proposed method consists of feature extraction, feature selection and classification stages. At first, acoustic features such as Root Mean Square energy (RMS), Mel-Frequency Cepstral Coefficients (MFCC) and Zero-crossing Rate are obtained from voice records. Subsequently, spectrogram images of the original sound signals are given as input to the pre-trained deep network architecture, which is VGG16, ResNet18, ResNet50, ResNet101, SqueezeNet and DenseNet201 and deep features are extracted. Thereafter, a hybrid feature vector is created by combining acoustic and deep features. Also, the ReliefF algorithm is used to select more efficient features from the hybrid feature vector. Finally, in order for the completion of the classification task, Support vector machine (SVM) is used. Experiments are made using three popular datasets used in the literature so as to evaluate the effect of various techniques. These datasets are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP). As a consequence, we reach to 79.41%, 90.21% and 85.37% accuracy rates for RAVDESS, EMO-DB, and IEMOCAP datasets, respectively. The Final results obtained in experiments, clearly, show that the proposed technique might be utilized to accomplish the task of speech emotion recognition efficiently. Moreover, when our technique is compared with those of methods used in the context, it is obvious that our method outperforms others in terms of classification accuracy rates.

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A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features

With an essential demand of human emotional behavior understanding and human machine interaction for the recent electronic applications, speaker emotion recognition is a key component which has attracted a great deal of attention among the researchers. Even though a handful of works are available in the literature for speaker emotion classification, the important challenges such as, distinct emotions, low quality recording, and independent affective states are still need to be addressed with good classifier and discriminative features. Accordingly, a new classifier, called fractional deep belief network (FDBN) is developed by combining deep belief network (DBN) and Fractional Calculus. This new classifier is trained with the multiple features such as tonal power ratio, spectral flux, pitch chroma and Mel frequency cepstral coefficients (MFCC) to make the emotional classes more separable through the spectral characteristics. The proposed FDBN classifier with integrated feature vectors is tested using two databases such as, Berlin database of emotional speech and real time Telugu database. The performance of the proposed FDBN and existing DBN classifiers are validated using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy. The experimental results obtained by the proposed FDBN shows the accuracy of 98.39 and 95.88 % in Berlin and Telugu database.

FDBN: Design and development of Fractional Deep Belief Networks for speaker emotion recognition

Speech recognition is a major topic in speech signal processing. Speech recognition is considered as one of the most popular and reliable biometric technologies used in automatic personal identification systems. Speech recognition systems are used for variety of applications such as multimedia browsing tool, access centre, security and finance. It allows people work in active environment to use computer. For a reliable and high accuracy of speech recognition, simple and efficient representation methods are required. In this paper, the zero crossing extraction and the energy level detection are applied to the recorded speech signal for voiced/unvoiced area detection. The detected voiced signals are applied for segmentation. Further, the MFCC method is applied to all of the segmented windows. The extracted MFCC data are further used as inputs for neural network training.

S. Yaacob

Papers

Mel-frequency cepstral coefficient analysis in speech recognition