A Probabilistic Generative Model for Spectral Speech Enhancement

TL;DR

This paper presents a probabilistic framework for speech enhancement in hearing aids that adapts to changing environments and user preferences using Bayesian inference, enabling real-time, personalized noise reduction.

Contribution

It introduces a unified probabilistic model that replaces fixed parameters with adaptive Bayesian inference for speech enhancement in hearing aids.

Findings

Achieves competitive speech quality and noise reduction with only 85 parameters.

Demonstrates real-time processing using variational message passing in a probabilistic programming environment.

Provides a flexible, interpretable foundation for adaptive hearing-aid signal processing.

Abstract

Speech enhancement in hearing aids remains a difficult task in nonstationary acoustic environments, mainly because current signal processing algorithms rely on fixed, manually tuned parameters that cannot adapt in situ to different users or listening contexts. This paper introduces a unified modular framework that formulates signal processing, learning, and personalization as Bayesian inference with explicit uncertainty tracking. The proposed framework replaces ad hoc algorithm design with a single probabilistic generative model that continuously adapts to changing acoustic conditions and user preferences. It extends spectral subtraction with principled mechanisms for in-situ personalization and adaptation to acoustic context. The system is implemented as an interconnected probabilistic state-space model, and inference is performed via variational message passing in the \texttt{RxInfer.jl} probabilistic programming environment, enabling real-time Bayesian processing under hearing-aid constraints. Proof-of-concept experiments on the \emph{VoiceBank+DEMAND} corpus show competitive speech quality and noise reduction with 85 effective parameters. The framework provides an interpretable, data-efficient foundation for uncertainty-aware, adaptive hearing-aid processing and points toward devices that learn continuously through probabilistic inference.