Kai Xu, Wei Zhang, Ming Zhang, You Wang, Guang‐hua Li
{"title":"无声语音识别的质量感知聚合共形预测","authors":"Kai Xu, Wei Zhang, Ming Zhang, You Wang, Guang‐hua Li","doi":"10.1145/3579654.3579762","DOIUrl":null,"url":null,"abstract":"As a kind of terminal neural signal, electromyography (EMG) generated by articulatory muscles is widely used in silent speech recognition (SSR). The SSR task can be converted to a pattern recognition problem, where silent speech patterns correspond to clusters in the feature space. Conventional inductive conformal predictor (ICP) based solutions in the SSR face limitations in the subsampling. In addition, the data quality assessment is a long-standing challenge on the recognition ability of models, but it is seldom discussed in previous works. We introduce the aggregated conformal prediction (ACP) to replace ICP, which enhances the diversity of subsampling to solve the problem. With the purpose of digging out the high-quality extended dataset in data expansion, this study proposes the Supervised K-means Evaluation (SKE) method. Equipped with SKE method, ACP framework contributes to an efficient and robust SSR solution, and the advantages have been validated on the task for Chinese words. Our results support it is significant to design SKE method and utilize ACP framework. To our knowledge, this study is the first to apply ACP methodology and quantitatively evaluate the data quality in the SSR task.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"355 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality-aware aggregated conformal prediction for silent speech recognition\",\"authors\":\"Kai Xu, Wei Zhang, Ming Zhang, You Wang, Guang‐hua Li\",\"doi\":\"10.1145/3579654.3579762\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a kind of terminal neural signal, electromyography (EMG) generated by articulatory muscles is widely used in silent speech recognition (SSR). The SSR task can be converted to a pattern recognition problem, where silent speech patterns correspond to clusters in the feature space. Conventional inductive conformal predictor (ICP) based solutions in the SSR face limitations in the subsampling. In addition, the data quality assessment is a long-standing challenge on the recognition ability of models, but it is seldom discussed in previous works. We introduce the aggregated conformal prediction (ACP) to replace ICP, which enhances the diversity of subsampling to solve the problem. With the purpose of digging out the high-quality extended dataset in data expansion, this study proposes the Supervised K-means Evaluation (SKE) method. Equipped with SKE method, ACP framework contributes to an efficient and robust SSR solution, and the advantages have been validated on the task for Chinese words. Our results support it is significant to design SKE method and utilize ACP framework. To our knowledge, this study is the first to apply ACP methodology and quantitatively evaluate the data quality in the SSR task.\",\"PeriodicalId\":146783,\"journal\":{\"name\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"volume\":\"355 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3579654.3579762\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3579654.3579762","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quality-aware aggregated conformal prediction for silent speech recognition
As a kind of terminal neural signal, electromyography (EMG) generated by articulatory muscles is widely used in silent speech recognition (SSR). The SSR task can be converted to a pattern recognition problem, where silent speech patterns correspond to clusters in the feature space. Conventional inductive conformal predictor (ICP) based solutions in the SSR face limitations in the subsampling. In addition, the data quality assessment is a long-standing challenge on the recognition ability of models, but it is seldom discussed in previous works. We introduce the aggregated conformal prediction (ACP) to replace ICP, which enhances the diversity of subsampling to solve the problem. With the purpose of digging out the high-quality extended dataset in data expansion, this study proposes the Supervised K-means Evaluation (SKE) method. Equipped with SKE method, ACP framework contributes to an efficient and robust SSR solution, and the advantages have been validated on the task for Chinese words. Our results support it is significant to design SKE method and utilize ACP framework. To our knowledge, this study is the first to apply ACP methodology and quantitatively evaluate the data quality in the SSR task.