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Real-Time Streamable Generative Speech Restoration with Flow Matching
Simon Welker, Bunlong Lay, Maris Hillemann +2
Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and spawning a new research direction. Their…
Are These Even Words? Quantifying the Gibberishness of Generative Speech Models
Danilo de Oliveira, Tal Peer, Jonas Rochdi +1
Significant research efforts are currently being dedicated to non-intrusive quality and intelligibility assessment, especially given how it enables curation of large scale datasets…
Diffusion Buffer for Online Generative Speech Enhancement
Bunlong Lay, Rostislav Makarov, Simon Welker +2
Online Speech Enhancement was mainly reserved for predictive models. A key advantage of these models is that for an incoming signal frame from a stream of data, the model is called…
Real-Time Streaming Mel Vocoding with Generative Flow Matching
Simon Welker, Tal Peer, Timo Gerkmann
The task of Mel vocoding, i.e., the inversion of a Mel magnitude spectrogram to an audio waveform, is still a key component in many text-to-speech (TTS) systems today. Based on gen…
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
Simon Welker, Lorenz Kuger, Tim Roith +4
In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions…
Are Modern Speech Enhancement Systems Vulnerable to Adversarial Attacks?
Rostislav Makarov, Lea Schönherr, Timo Gerkmann
Machine learning approaches for speech enhancement are becoming increasingly expressive, enabling ever more powerful modifications of input signals. In this paper, we demonstrate t…
Enhancing In-the-Wild Speech Emotion Conversion with Resynthesis-based Duration Modeling
Navin Raj Prabhu, Danilo de Oliveira, Nale Lehmann-Willenbrock +1
Speech Emotion Conversion aims to modify the emotion expressed in input speech while preserving lexical content and speaker identity. Recently, generative modeling approaches have…
Diffusion Buffer: Online Diffusion-based Speech Enhancement with Sub-Second Latency
Bunlong Lay, Rostislav Makarov, Timo Gerkmann
Diffusion models are a class of generative models that have been recently used for speech enhancement with remarkable success but are computationally expensive at inference time. T…
ReverbFX: A Dataset of Room Impulse Responses Derived from Reverb Effect Plugins for Singing Voice Dereverberation
Julius Richter, Till Svajda, Timo Gerkmann
We present ReverbFX, a new room impulse response (RIR) dataset designed for singing voice dereverberation research. Unlike existing datasets based on real recorded RIRs, ReverbFX f…
LipDiffuser: Lip-to-Speech Generation with Conditional Diffusion Models
Julius Richter, Danilo de Oliveira, Tal Peer +1
We present LipDiffuser, a conditional diffusion model for lip-to-speech generation synthesizing natural and intelligible speech directly from silent video recordings. Our approach…
Do We Need EMA for Diffusion-Based Speech Enhancement? Toward a Magnitude-Preserving Network Architecture
Julius Richter, Danilo de Oliveira, Timo Gerkmann
We study diffusion-based speech enhancement using a Schrodinger bridge formulation and extend the EDM2 framework to this setting. We employ time-dependent preconditioning of networ…
FlowDec: A flow-based full-band general audio codec with high perceptual quality
Simon Welker, Matthew Le, Ricky T. Q. Chen +4
We propose FlowDec, a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel co…