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Found 8 papers, 3 papers with code
A Comparative Cross Language View On Acted Databases Portraying Basic Emotions Utilising Machine Learning
Since several decades emotional databases have been recorded by various laboratories.
Nkululeko: A Tool For Rapid Speaker Characteristics Detection
We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain.
Testing Speech Emotion Recognition Machine Learning Models
Machine learning models for speech emotion recognition (SER) can be trained for different tasks and are usually evaluated on the basis of a few available datasets per task.
audb -- Sharing and Versioning of Audio and Annotation Data in Python
Driven by the need for larger and more diverse datasets to pre-train and fine-tune increasingly complex machine learning models, the number of datasets is rapidly growing.
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge
Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Dawn of the transformer era in speech emotion recognition: closing the valence gap
Recent advances in transformer-based architectures which are pre-trained in self-supervised manner have shown great promise in several machine learning tasks.
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks
Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals.
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF).
