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Found 8 papers, 0 papers with code
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement
Self-supervised learned models have been found to be very effective for certain speech tasks such as automatic speech recognition, speaker identification, keyword spotting and others.
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition
In parallel to these models, researchers have proposed several data augmentation techniques to increase the size and variability of existing labeled datasets.
A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion
We term this new architecture a variational CycleGAN (VCGAN).
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain
We construct a Knowledge Graph from Aircraft Accident reports and contribute this resource to the community of researchers.
Adaptive Speech Duration Modification using a Deep-Generative Framework
We propose the first method to adaptively modify the duration of a given speechsignal.
A Deep-Bayesian Framework for Adaptive Speech Duration Modification
During inference, we generate the attention map as a proxy for the similarity matrix between the given input speech and an unknown target speech signal.
Multi-speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network
Finally, the predictor uses the original spectrum and the modified F0 contour to generate a corresponding target spectrum.
Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator
We introduce a novel method for emotion conversion in speech that does not require parallel training data.
