learning discriminative features from spectrograms using center loss for speech emotion recognition
Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating softmax cross-entropy loss and center loss together. The softmax cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3\% on Mel-spectrogram input, and more than 4\% on Short Time Fourier Transform spectrogram input.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionSpeech Emotion RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition
This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and center loss, a powerful metric learning s…
Emotion RecognitionMetric LearningSpeech Emotion RecognitionA Survey of Deep Learning for Complex Speech Spectrograms
Recent advancements in deep learning have significantly impacted the field of speech signal processing, particularly in the analysis and manipulation of complex spectrograms. This survey provides a comprehensive overview…
Deep LearningSpeech EnhancementSpeech SeparationSurveyExplaining Spectrograms in Machine Learning: A Study on Neural Networks for Speech Classification
This study investigates discriminative patterns learned by neural networks for accurate speech classification, with a specific focus on vowel classification tasks. By examining the activations and features of neural netw…
Classificationspeech-recognitionSpeech RecognitionVowel ClassificationTriplet-Center Loss for Multi-View 3D Object Retrieval
Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with …
3D Object Recognition3D Object Retrieval3D Shape Classification3D Shape Retrieval+6Adaptive Neuron-wise Discriminant Criterion and Adaptive Center Loss at Hidden Layer for Deep Convolutional Neural Network
A deep convolutional neural network (CNN) has been widely used in image classification and gives better classification accuracy than the other techniques. The softmax cross-entropy loss function is often used for classif…
Face RecognitionGeneral Classificationimage-classificationImage Classification