xuanjihe/speech-emotion-recognition
TensorFlow implementation of 3-D Convolutional Recurrent Neural Networks for classifying emotions in speech audio on the IEMOCAP dataset.
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This repository provides a neural network model for speech emotion recognition (SER), classifying utterances into emotional categories using convolutional and recurrent layers combined with attention-based pooling. It processes audio features (mel-spectrograms) through 3-D convolution and bidirectional RNN layers, then aggregates frame-level features using max/mean/attention pooling to produce utterance-level predictions. The model was developed for the IEMOCAP benchmark dataset and achieved recognition rates reported in a 2018 IEEE Signal Processing Letters paper.
Frequently asked
- What is xuanjihe/speech-emotion-recognition?
- TensorFlow implementation of 3-D Convolutional Recurrent Neural Networks for classifying emotions in speech audio on the IEMOCAP dataset.
- Is speech-emotion-recognition open source?
- Yes — xuanjihe/speech-emotion-recognition is an open-source project tracked on heatdrop.
- What language is speech-emotion-recognition written in?
- xuanjihe/speech-emotion-recognition is primarily written in Python.
- How popular is speech-emotion-recognition?
- xuanjihe/speech-emotion-recognition has 409 stars on GitHub.
- Where can I find speech-emotion-recognition?
- xuanjihe/speech-emotion-recognition is on GitHub at https://github.com/xuanjihe/speech-emotion-recognition.