Objective Speech recognition technology is widely used as a mature technical approach in many fields.In the study of depression recognition,speech signals are commonly used due to their convenience and ease of acquisi...Objective Speech recognition technology is widely used as a mature technical approach in many fields.In the study of depression recognition,speech signals are commonly used due to their convenience and ease of acquisition.Though speech recognition is popular in the research field of depression recognition,it has been little studied in somatisation disorder recognition.The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies.To this end,we introduced our somatisation disorder speech database and gave benchmark results.Methods By collecting speech samples of somatisation disorder patients,in cooperation with the Shenzhen University General Hospital,we introduced our somatisation disorder speech database,the Shenzhen Somatisation Speech Corpus(SSSC).Moreover,a benchmark for SSSC using classic acoustic features and a machine learning model was proposed in our work.Results To obtain a more scientific benchmark,we compared and analysed the performance of different acoustic features,i.e.,the full ComPare feature set,or only Mel frequency cepstral coefficients(MFCCs),fundamental frequency(F0),and frequency and bandwidth of the formants(F1-F3).By comparison,the best result of our benchmark was the 76.0%unweighted average recall achieved by a support vector machine with formants F1–F3.Conclusion The proposal of SSSC may bridge a research gap in somatisation disorder,providing researchers with a publicly accessible speech database.In addition,the results of the benchmark could show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders.展开更多
基金supported by the Ministry of Science and Technology of the People's Republic of China with the STI2030-Major Projects(Grant No.2021ZD0201900)the National Natural Science Foundation of China(Grant Nos.62227807 and 62272044)+3 种基金the Teli Young Fellow Program from the Beijing Institute of Technology,the Shenzhen Municipal Scheme for Basic Research(Grant Nos.JCYJ20210324100208022andJCYJ20190808144005614),Chinathe JSPS KAKENHI(Grant No.20H00569)the JST Mirai Program(Grant No.21473074)the JST MOONSHOT Program(Grant No.JPMJMS229B).
文摘Objective Speech recognition technology is widely used as a mature technical approach in many fields.In the study of depression recognition,speech signals are commonly used due to their convenience and ease of acquisition.Though speech recognition is popular in the research field of depression recognition,it has been little studied in somatisation disorder recognition.The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies.To this end,we introduced our somatisation disorder speech database and gave benchmark results.Methods By collecting speech samples of somatisation disorder patients,in cooperation with the Shenzhen University General Hospital,we introduced our somatisation disorder speech database,the Shenzhen Somatisation Speech Corpus(SSSC).Moreover,a benchmark for SSSC using classic acoustic features and a machine learning model was proposed in our work.Results To obtain a more scientific benchmark,we compared and analysed the performance of different acoustic features,i.e.,the full ComPare feature set,or only Mel frequency cepstral coefficients(MFCCs),fundamental frequency(F0),and frequency and bandwidth of the formants(F1-F3).By comparison,the best result of our benchmark was the 76.0%unweighted average recall achieved by a support vector machine with formants F1–F3.Conclusion The proposal of SSSC may bridge a research gap in somatisation disorder,providing researchers with a publicly accessible speech database.In addition,the results of the benchmark could show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders.