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This paper describes noisy speech recognition for an augmented realityheadset that helps verbal communication within real multiparty conversationalenvironments. A major approach that has actively been studied in simulatedenvironments is to sequentially perform speech enhancement and automatic speechrecognition (ASR) based on deep neural networks (DNNs) trained in a supervisedmanner. In our task, however, such a pretrained system fails to work due to themismatch between the training and test conditions and the head movements of theuser. To enhance only the utterances of a target speaker, we use beamformingbased on a DNN-based speech mask estimator that can adaptively extract thespeech components corresponding to a head-relative particular direction. Wepropose a semi-supervised adaptation method that jointly updates the maskestimator and the ASR model at run-time using clean speech signals withground-truth transcriptions and noisy speech signals with highly-confidentestimated transcriptions. Comparative experiments using the state-of-the-artdistant speech recognition system show that the proposed method significantlyimproves the ASR performance.

Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments