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In this work, we present a causal speech enhancement system that is designed to handle different types of corruptions. This paper is an extended version of our contribution to the &#x201C;ICASSP 2023 Speech Signal Improvement Challenge&#x201D;. The method is based on a generative diffusion model which has been shown to work well in scenarios beyond speech-in-noise, such as missing data and non-additive corruptions. We guarantee causal processing with an algorithmic latency of 20&#x00A0;ms by modifying the network architecture and removing non-causal normalization techniques. To train and test our model, we generate a new corrupted speech dataset which includes additive background noise, reverberation, clipping, packet loss, bandwidth reduction, and codec artifacts. We compare the causal and non-causal versions of our method to investigate the impact of causal processing and we assess the gap between specialized models trained on a particular corruption type and the generalized model trained on all corruptions. Although specialized models and non-causal models have a small advantage, we show that the generalized causal approach does not suffer from a significant performance penalty, while it can be flexibly employed for real-world applications where different types of distortions may occur.

Causal Diffusion Models for Generalized Speech Enhancement