Feature Specific Hybrid Framework on composition of Deep learning architecture for speech emotion recognition
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No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Post-publication record
- Nature
- Retraction
- Reason
- Concerns/Issues about Referencing/Attributions;Compromised Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Computer-Aided Content or Computer-Generated Content;
- Date
- 2/23/2022 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
Abstract Speech cues may be used to identify human emotions using deep learning model of speech emotion recognition using supervised learning or unsupervised learning as machine learning concepts, and then it build the speech emotion databases for test data prediction. Despite of many advantageous, still it suffers from accuracy and other aspects. In order to mitigate those issues, we propose a new feature specific hybrid framework on composition of deep learning architecture such as recurrent neural network and convolution neural network for speech emotion recognition. It analyses different characteristics to make a better description of speech emotion. Initially it uses feature extraction technique using bag-of-Audio-word model to Mel-frequency cepstral factor characteristics and a pack of acoustic words composed of emotion features to feed the hybrid deep learning architecture to result in high classification and prediction accuracy. In addition, the proposed hybrid networks’ output is concatenated and loaded into this layer of softmax, which produces a for speech recognition, a categorical classification statistic is used. The proposed model is based on the Ryerson Audio-Visual Database of Emotional Speech and Song audio (RAVDESS) dataset, which comprises eight emotional groups. Experimental results on dataset prove that proposed framework performs better in terms of 89.5% recognition rate and 98% accuracy against state of art approaches.
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The record
- Venue
- Journal of Physics Conference Series
- Topic
- Emotion and Mood Recognition
- Field
- Psychology
- Canadian institutions
- —
- Funders
- —
- Keywords
- Computer scienceSoftmax functionSpeech recognitionArtificial intelligenceDeep learningFeature (linguistics)Feature extractionFeature learningArtificial neural networkMel-frequency cepstrumCategorical variablePattern recognition (psychology)Machine learning
- Has abstract in OpenAlex
- yes