A review of deep learning techniques for speech processing
The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …
learning. The use of multiple processing layers has enabled the creation of models capable …
High fidelity neural audio compression
We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural
networks. It consists in a streaming encoder-decoder architecture with quantized latent …
networks. It consists in a streaming encoder-decoder architecture with quantized latent …
[PDF][PDF] The age of synthetic realities: Challenges and opportunities
Synthetic realities are digital creations or augmentations that are contextually generated
through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data …
through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data …
Speech enhancement and dereverberation with diffusion-based generative models
In this work, we build upon our previous publication and use diffusion-based generative
models for speech enhancement. We present a detailed overview of the diffusion process …
models for speech enhancement. We present a detailed overview of the diffusion process …
A survey on audio diffusion models: Text to speech synthesis and enhancement in generative ai
Generative AI has demonstrated impressive performance in various fields, among which
speech synthesis is an interesting direction. With the diffusion model as the most popular …
speech synthesis is an interesting direction. With the diffusion model as the most popular …
Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation
Diffusion models have shown a great ability at bridging the performance gap between
predictive and generative approaches for speech enhancement. We have shown that they …
predictive and generative approaches for speech enhancement. We have shown that they …
HierSpeech: Bridging the gap between text and speech by hierarchical variational inference using self-supervised representations for speech synthesis
This paper presents HierSpeech, a high-quality end-to-end text-to-speech (TTS) system
based on a hierarchical conditional variational autoencoder (VAE) utilizing self-supervised …
based on a hierarchical conditional variational autoencoder (VAE) utilizing self-supervised …
CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …
starvation and electronic noise. Recently, some works have attempted to use diffusion …
Analysing diffusion-based generative approaches versus discriminative approaches for speech restoration
Diffusion-based generative models have had a high impact on the computer vision and
speech processing communities these past years. Besides data generation tasks, they have …
speech processing communities these past years. Besides data generation tasks, they have …
APNet: An all-frame-level neural vocoder incorporating direct prediction of amplitude and phase spectra
This paper presents a novel neural vocoder named APNet which reconstructs speech
waveforms from acoustic features by predicting amplitude and phase spectra directly. The …
waveforms from acoustic features by predicting amplitude and phase spectra directly. The …