Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition
Detecting emotions directly from a speech signal plays an important role in effective human-computer interactions. Existing speech emotion recognition models require massive computational and storage resources, making them hard to implement concurrently with other machine-interactive tasks in embedded systems. In this paper, we propose an efficient and lightweight fully convolutional neural network for speech emotion recognition in systems with limited hardware resources. In the proposed FCNN model, various feature maps are extracted via three parallel paths with different filter sizes. This helps deep convolution blocks to extract high-level features, while ensuring sufficient separability. The extracted features are used to classify the emotion of the input speech segment. While our model has a smaller size than that of the state-of-the-art models, it achieves higher performance on the IEMOCAP and EMO-DB datasets.
Code (1)
Tasks
Emotion RecognitionSpeech Emotion RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition
Despite notable progress, speech emotion recognition (SER) remains challenging due to the intricate and ambiguous nature of speech emotion, particularly in wild world. While current studies primarily focus on recognition…
AttributeCross-corpusEmotion RecognitionOut-of-Distribution Detection+1PoserNet: Refining Relative Camera Poses Exploiting Object Detections
The estimation of the camera poses associated with a set of images commonly relies on feature matches between the images. In contrast, we are the first to address this challenge by using objectness regions to guide the p…
Graph Neural NetworkObjectPose EstimationUL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search
Lightweight models are essential for real-time speech enhancement applications. In recent years, there has been a growing trend toward developing increasingly compact models for speech enhancement. In this paper, we prop…
Neural Architecture SearchSpeech EnhancementLaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sen…
3D Object DetectionAutonomous Drivingobject-detectionObject DetectionAudio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separation in noisy environments. To this end, w…
Speech Separation