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Extending RNN-T-based speech recognition systems with emotion and language classification
We propose using a recurrent neural network transducer (RNN-T)-based speech-to-text (STT) system as a common component that can be used for emotion recognition and language identification as well as for speech recognition.
Towards a Common Speech Analysis Engine
Beyond that, a common engine should be capable of supporting distributed training with client in-house private data.
Speech Emotion Recognition using Self-Supervised Features
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speech emotion recognition (SER) still need further investigation.
Speaker Normalization for Self-supervised Speech Emotion Recognition
Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases.
Ranked #1 on
Speech Emotion Recognition
on IEMOCAP
(AUC metric)
Siamese x-vector reconstruction for domain adapted speaker recognition
With the rise of voice-activated applications, the need for speaker recognition is rapidly increasing.
