{"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/generative-adversarial-training-data","title":"Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition","arxiv_id":"2005.09256","date":"2020-05-19","proceeding":null,"authors":["Kohei Matsuura","Masato Mimura","Shinsuke Sakai","Tatsuya Kawahara"],"abstract":"It is important to transcribe and archive speech data of endangered languages for preserving heritages of verbal culture and automatic speech recognition (ASR) is a powerful tool to facilitate this process. However, since endangered languages do not generally have large corpora with many speakers, the performance of ASR models trained on them are considerably poor in general. Nevertheless, we are often left with a lot of recordings of spontaneous speech data that have to be transcribed. In this work, for mitigating this speaker sparsity problem, we propose to convert the whole training speech data and make it sound like the test speaker in order to develop a highly accurate ASR system for this speaker. For this purpose, we utilize a CycleGAN-based non-parallel voice conversion technology to forge a labeled training data that is close to the test speaker's speech. We evaluated this speaker adaptation approach on two low-resource corpora, namely, Ainu and Mboshi. We obtained 35-60% relative improvement in phone error rate on the Ainu corpus, and 40% relative improvement was attained on the Mboshi corpus. This approach outperformed two conventional methods namely unsupervised adaptation and multilingual training with these two corpora.","url_abs":"https://arxiv.org/abs/2005.09256v2","url_pdf":"https://arxiv.org/pdf/2005.09256v2.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":[{"paper_slug":"generative-adversarial-training-data","repo_url":"https://github.com/Kohei-Matsuura/Non-parallel-VC-on-Mboshi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"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":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"voice-conversion","task_name":"Voice Conversion"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}