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With the recent surge of video conferencing tools usage, providinghigh-quality speech signals and accurate captions have become essential toconduct day-to-day business or connect with friends and families.Single-channel personalized speech enhancement (PSE) methods show promisingresults compared with the unconditional speech enhancement (SE) methods inthese scenarios due to their ability to remove interfering speech in additionto the environmental noise. In this work, we leverage spatial informationafforded by microphone arrays to improve such systems' performance further. Weinvestigate the relative importance of speaker embeddings and spatial features.Moreover, we propose a new causal array-geometry-agnostic multi-channel PSEmodel, which can generate a high-quality enhanced signal from arbitrarymicrophone geometry. Experimental results show that the proposed geometryagnostic model outperforms the model trained on a specific microphone arraygeometry in both speech quality and automatic speech recognition accuracy. Wealso demonstrate the effectiveness of the proposed approach for unseen arraygeometries.

One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement