arXiv subjects

Jacob J Webber

Publications and source records attributed to Jacob J Webber.

3 recordsLinked to original sources

arXiv2025

Comparator Loss: An Ordinal Contrastive Loss to Derive a Severity Score for Speech-based Health Monitoring

Monitoring the progression of neurodegenerative disease (NDD) has important applications in planning treatment and evaluating new medications. Whereas much work has focused on discriminating patients from healthy controls, or predicting real-world health metrics, we propose a novel measure of disease progression: the severity score, derived from a model trained to minimize what we call the comparator loss. This loss ensures scores obey an ordering relation, based on diagnosis, clinical scores, or simply chronological order of recordings. The proposed comparator loss-based system has the potential to incorporate information from disparate health metrics, critical for making full use of small health-related datasets. We show that a model trained on lightly annotated data is capable of distinguishing between subjects with NDDs and healthy controls. Our score also correlates with annotations not observed in training, such as ALSFRS-R and those of speech and language therapists.

eess.AS↗

arXiv2024

Voice Conversion-based Privacy through Adversarial Information Hiding

Privacy-preserving voice conversion aims to remove only the attributes of speech audio that convey identity information, keeping other speech characteristics intact. This paper presents a mechanism for privacy-preserving voice conversion that allows controlling the leakage of identity-bearing information using adversarial information hiding. This enables a deliberate trade-off between maintaining source-speech characteristics and modification of speaker identity. As such, the approach improves on voice-conversion techniques like CycleGAN and StarGAN, which were not designed for privacy, meaning that converted speech may leak personal information in unpredictable ways. Our approach is also more flexible than ASR-TTS voice conversion pipelines, which by design discard all prosodic information linked to textual content. Evaluations show that the proposed system successfully modifies perceived speaker identity whilst well maintaining source lexical content.

cs.SD↗

arXiv2023

Autovocoder: Fast Waveform Generation from a Learned Speech Representation using Differentiable Digital Signal Processing

Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an intermediate representation, to decompose the task into acoustic modelling and waveform generation. A mel-spectrogram is extracted from the waveform by a simple, fast DSP operation, but generating a high-quality waveform from a mel-spectrogram requires computationally expensive machine learning: a neural vocoder. Our proposed ``autovocoder'' reverses this arrangement. We use machine learning to obtain a representation that replaces the mel-spectrogram, and that can be inverted back to a waveform using simple, fast operations including a differentiable implementation of the inverse STFT. The autovocoder generates a waveform 5 times faster than the DSP-based Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We provide perceptual listening test results to confirm that the speech is of comparable quality to HiFi-GAN in the copy synthesis task.

cs.SD↗