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- W4320175698 abstract "Accomplishments in the field of artificial intelligence are utilized in the advancement of computing and making of intelligent machines for facilitating mankind and improving user experience. Emotions are rudimentary for people, affecting thinking and ordinary exercises like correspondence, learning and direction. Speech emotion recognition is domain of interest in this regard and in this work, we propose a novel mel spectrogram learning approach in which our model uses the datapoints to learn emotions from the given wav form voice notes in the popular CREMA-D dataset. Our model uses log mel-spectrogram as feature with number of mels = 64. It took less training time compared to other approaches used to address the problem of emotion speech recognition." @default.
- W4320175698 created "2023-02-13" @default.
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- W4320175698 date "2022-06-10" @default.
- W4320175698 modified "2023-09-23" @default.
- W4320175698 title "AHD ConvNet for Speech Emotion Classification" @default.
- W4320175698 doi "https://doi.org/10.48550/arxiv.2206.05286" @default.
- W4320175698 hasPublicationYear "2022" @default.
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