{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/direction-aware-joint-adaptation-of-neural","title":"Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments","arxiv_id":"2207.07273","date":"2022-07-15","proceeding":null,"authors":["Yicheng Du","Aditya Arie Nugraha","Kouhei Sekiguchi","Yoshiaki Bando","Mathieu Fontaine","Kazuyoshi Yoshii"],"abstract":"This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. A major approach that has actively been studied in simulated environments is to sequentially perform speech enhancement and automatic speech recognition (ASR) based on deep neural networks (DNNs) trained in a supervised manner. In our task, however, such a pretrained system fails to work due to the mismatch between the training and test conditions and the head movements of the user. To enhance only the utterances of a target speaker, we use beamforming based on a DNN-based speech mask estimator that can adaptively extract the speech components corresponding to a head-relative particular direction. We propose a semi-supervised adaptation method that jointly updates the mask estimator and the ASR model at run-time using clean speech signals with ground-truth transcriptions and noisy speech signals with highly-confident estimated transcriptions. Comparative experiments using the state-of-the-art distant speech recognition system show that the proposed method significantly improves the ASR performance.","url_abs":"https://arxiv.org/abs/2207.07273v1","url_pdf":"https://arxiv.org/pdf/2207.07273v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"automatic-speech-recognition-2","task_name":"Automatic Speech Recognition"},{"task_slug":"automatic-speech-recognition","task_name":"Automatic Speech Recognition (ASR)"},{"task_slug":"distant-speech-recognition","task_name":"Distant Speech Recognition"},{"task_slug":"noisy-speech-recognition","task_name":"Noisy Speech Recognition"},{"task_slug":"speech-enhancement","task_name":"Speech Enhancement"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-enhancement-on-easycom","task":"Speech Enhancement","dataset":"EasyCom","model":"DAJA (MVDR,HMA,1000) (Overlapped Speech)","rank_in_archive_order":6,"of":6,"metrics":{"SDR":"-4.76"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-easycom","task":"Speech Recognition","dataset":"EasyCom","model":"DAJA (MVDR,HMA,1000) (Overlapped Speech)","rank_in_archive_order":3,"of":5,"metrics":{"WER (%)":"62.36"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}