基于语音识别技术的失语症辅助诊断及康复治疗系统的研究
|
摘要
失语症患者的康复治疗问题越来越引起人们的关注。传统的失语症诊断及康复治疗都需要治疗师的参与,特别是在言语方面的诊断及训练更需要治疗师的主观判断,致使评估结果缺乏客观的定量和定性指标。随着计算机科学技术的发展,特别是语音识别技术的日趋成熟,设计一套智能化、小型化、可灵活扩展的失语症辅助诊断和康复治疗为一体的系统软件是失语症康复治疗研究的一个重要方向。
本课题主要论述了失语症辅助诊断及康复治疗系统的分析、设计及建立。重点论述基于语音识别技术和人工神经网络的失语症语音评估系统的实现。通过对系统需求的分析,设计出合理的数据库。对目前流行软件开发语言的分析,确定系统的开发方式。最后,通过图片及表格的方式来显示系统建立的情况。
本课题主要的研究工作包括:关于语音信号的端点检测及声韵母的切分算法、基于隐马尔可夫模型(HMM)和BP神经网络的失语症语音评估系统的研究、语音信号信息量和流利性的评估及人工智能调度功能的设计思想。在这些研究的基础上,最后构建出失语症辅助诊断及康复治疗系统,并实现了病历信息子系统、失语症辅助诊断子系统、康复治疗子系统及训练资料库子系统之间的联系。
Aphasia rehabilitation treatment problems have received much attention. The methods of traditional aphasia diagnosis and rehabilitation are accomplished with the help of therapists. The diagnosis and treatment mainly depend on the subjective judgment of therapists, which is lack of an objective assessment of quantitative and qualitative indicators, especially in the area of speech training. As the fast development of computer and the technology mature in speech recognition, designing a set of system software that is intelligent, small size, and scalable for the aphasia diagnosis and rehabilitation treatment is becoming one of the important directions of aphasia rehabilitation.
The main contents described in the dissertation are the diagnosis and rehabilitation treatment system of aphasia about analysis, design and implementation. It is focus on the aphasia speech evaluation system based on speech recognition technology and artificial neural network. The reasonable database is devised by analyzing the system’s demands and the development approach of system is decided through the analysis of current software development language. At the end, the achievement of system that is designed by the author is showed in the ways of pictures and tables.
The major research in the dissertation is summarized as follows:
1) Proposed a new and reasonable scheme on the algorithm of the endpoint detection and segmentation between consonants and vowels based on the system’s demands; 2) Studied hard on the aphasia speech evaluation system based on Hidden Markov Model (HMM) and BP neural network and the assessment of speech signal fluency and information; 3) Introduced a new idea of artificial intelligence and scheduling function in the designed system; 4) On the basis of those, constituted the diagnosis and rehabilitation treatment system of aphasia, and made the medical information subsystem, the diagnosis of aphasia subsystems, the rehabilitation subsystem and the training database subsystem come true .
本课题主要论述了失语症辅助诊断及康复治疗系统的分析、设计及建立。重点论述基于语音识别技术和人工神经网络的失语症语音评估系统的实现。通过对系统需求的分析,设计出合理的数据库。对目前流行软件开发语言的分析,确定系统的开发方式。最后,通过图片及表格的方式来显示系统建立的情况。
本课题主要的研究工作包括:关于语音信号的端点检测及声韵母的切分算法、基于隐马尔可夫模型(HMM)和BP神经网络的失语症语音评估系统的研究、语音信号信息量和流利性的评估及人工智能调度功能的设计思想。在这些研究的基础上,最后构建出失语症辅助诊断及康复治疗系统,并实现了病历信息子系统、失语症辅助诊断子系统、康复治疗子系统及训练资料库子系统之间的联系。
Aphasia rehabilitation treatment problems have received much attention. The methods of traditional aphasia diagnosis and rehabilitation are accomplished with the help of therapists. The diagnosis and treatment mainly depend on the subjective judgment of therapists, which is lack of an objective assessment of quantitative and qualitative indicators, especially in the area of speech training. As the fast development of computer and the technology mature in speech recognition, designing a set of system software that is intelligent, small size, and scalable for the aphasia diagnosis and rehabilitation treatment is becoming one of the important directions of aphasia rehabilitation.
The main contents described in the dissertation are the diagnosis and rehabilitation treatment system of aphasia about analysis, design and implementation. It is focus on the aphasia speech evaluation system based on speech recognition technology and artificial neural network. The reasonable database is devised by analyzing the system’s demands and the development approach of system is decided through the analysis of current software development language. At the end, the achievement of system that is designed by the author is showed in the ways of pictures and tables.
The major research in the dissertation is summarized as follows:
1) Proposed a new and reasonable scheme on the algorithm of the endpoint detection and segmentation between consonants and vowels based on the system’s demands; 2) Studied hard on the aphasia speech evaluation system based on Hidden Markov Model (HMM) and BP neural network and the assessment of speech signal fluency and information; 3) Introduced a new idea of artificial intelligence and scheduling function in the designed system; 4) On the basis of those, constituted the diagnosis and rehabilitation treatment system of aphasia, and made the medical information subsystem, the diagnosis of aphasia subsystems, the rehabilitation subsystem and the training database subsystem come true .
引文
[1]黄东峰.临床康复医学.广东:汕头大学出版社, 2004.
[2] Paradis著,林谷辉,林梅溪,陈卓铭译.双语失语症的评估.广州:暨南大学出版社,2003.
[3]唐菱.失语症分类研究概述[J].湖南大学学报, 2003, (17) pp: 100-103.
[4]高素荣.《失语症》[M].北京:北京医科大学、中国协和医科大学联合出版社,1993.
[5]王德春,吴本虎,王德林.《神经语言学》[M].上海:上海外语教育出版社,1997.
[6]罗倩,彭聃龄.失语症的语言学研究综述[J].当代语言学, 2000, 2(4) pp: 248-263.
[7]曹京波,赵纯,金旻,张玉梅.失语症的常用评价方法[J].中国临床康复, 2006, (10) pp:139-141
[8] Akbarzadeh-T, Mohammad-R., Moshtagh-Khorasani, et al. A hierarchical fuzzy rule-based approach to aphasia diagnosis[J]. Journal of Biomedical Informatics. 2007, 40(5) pp: 465-475.
[9] Bongar, Szabolcs. A novel computer-aided neurolinguistic approach to the treatment of aphasia[J]. Period Polytech Electr Eng. 2006, 50(1-2) pp: 115-128
[10] Larsson Inger, Thoren-Jonsson, Anna-Lisa. The Swedish Speech Interpretation Service: An exploratory study of a new communication support provided to people with aphasia[J]. AAC: Augmentative and Alternative Communication.2007, 23(4) pp: 312-322
[11] Masson Veronique, Quiniou Rene. Application of artificial intelligence of aphasia treatment.Proceedings of the 3rd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. 1991, pp: 907-913.
[12] Moffatt Karyn, McGrenere Joanna, Purves Barbara, et al .The participatory design of a sound and image enhanced daily planner for people with Aphasia.2004 Conference on Human Factors in Computing Systems - Proceedings, CHI.2004, pp: 407-414
[13] Raymer Anastasia, Kohen Francine. Word-retrieval treatment in aphasia: Effects of sentence context[J]. Journal of Rehabilitation Research and Development, 2006, 43(3)pp: 367-377
[14] Weinrich M, Shelton JR, McCall K, et al. Generalization from single sentence to multisentence production in severely aphasic patients[J]. Brain Lang. 1997, (58) pp: 327-352
[15]李巧薇,陈卓铭,黄舜韶.计算机在辅助认知障碍诊断和康复中的应用[J].中国康复理论与实践, 2002, 8(3) pp: 147-148.
[16]何湘智.语音识别的研究与发展,计算机与现代化, 2002, (3)pp: 3-6.
[17]汪洁.失语症的治疗及其进展.实用老年医学,2003, 17(1) pp: 13-15.
[18]陈卓铭,黄舜韶,李巧薇.基于计算机运算特点的语言障碍的分类.中国临床康复.2003,7(7) pp: 1133-1135.
[19]陈卓铭,唐桂华,莫雷等.语言障碍诊治仪ZM2.1的复测信度分析.中国临床康复. 2004, 8(16) pp: 3025-3027.
[20]秦冰,申建国.失语症计算机评定系统的设计与临床应用[J].中国临床康复, 2005, 9 (12)pp:48-49.
[21]王荫华.西方失语症成套测验(WAB)介绍(一)[J].中国康复理论与实践, 1997, (3)pp: 87-89.
[22]王荫华.西方失语症成套测验(WAB)介绍(二)[J].中国康复理论与实践, 1997, (3)pp: 135-140.
[23]陈伶俐,刘晓加.浅谈失语病人的康复训练[J].护理学杂志, 2004, 9(1) pp: 78-80.
[24]柏强.浅析失语症的康复治疗[J].中医药学刊, 2006, 24(4) pp: 707-708.
[25]韩纪庆,张磊,郑铁然等.语音信号处理[M].北京:清华大学出版社, 2005.
[26] Bing-Fei Wu, and Kun-Ching Wang. Robust Endpoint Detection Algorithm Based on the Adaptive Band-Partitioning Spectral Entropy in Adverse Environments. IEEE Trans.on speech and audio processing, 2005, 13(5) pp: 762-775.
[27] C. T. Lin, J. Y. Lin, and G. D. Wu. A robust word boundary detection algorithm for variable noise-level environment in cars. IEEE Trans. Intell.Transp. Syst., 2002, 3(1) pp: 89–101.
[28] Laska AC, Hellblom A, Murray V, et al. Aphasia in acute stroke and relation to outcome. J Intern Med 2001, 24(9) pp: 413–422.
[29] Pedersen PM, Vinter K, Olsen TS.Aphasia after stroke type.severity and prognosis.The Copenhagen aphasia study. Cerebrovasc Dis, 2004, 17(1) pp: 35-43..
[30] Van de, Sandt-KOendeman M, Wiegers J, et al. A computedsed communication aid for people with aphasia[J]. Disabtl Rehabil , 2005, 27(9) pp: 529-533
[31]杨胜跃,周宴宇,黄深喜.语音信号端点检测方法与展望[J].信息技术, 2005, (7)pp: 5-8.
[32]崔冬青,李治柱,吴亚栋.一种噪声环境下连续语音识别的快速端点检测算法[J].计算机工程与应用, 2003, (23) pp: 95-97.
[33]郭巧,张立伟,陆际联.用于汉语语音信号端点检测与切分的有效方法[J].计算机工程与应用. 2000, (5) pp: 92-94.
[34]董力,陈宏钦,马争鸣.基于小波变换的语音段起止端点检测算法[J].中山大学学报(自然科学版), 2005, 44(3) pp: 116-118
[35]赵力.语音信号处理[M].北京,机械工业出版社, 2003.
[36]候雪梅,张雪英,赵高峰.一种改进的基于LP倒谱特征的孤立词语音识别方法[J].太原理工大学学报,2006, (5) pp: 508-510.
[37]陈立伟.基于HMM和ANN的汉语语音识别[D].哈尔滨工程大学博士学位论文.2005
[38]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社, 2003
[39] Wade J, Petheram B, Cain R.Voice recognition and aphasia: can computer understand aphasic speech. Disabil Rehabil, 2001, 23(14) pp: 604-613
[40] Robert Hecht - Nielson. Theory of the Back - Propagation Neural Network[C]. IJCNN, 1989, 1: 583-604
[41] White H.Commentionist nonparametric regression:Multilayerfeedforward networks can learn arbitrary mapping[J].Neural Networks,1990,(3)
[42]李涛,陈卓铭,尹义臣等.计算机测定失语症语速的相关分析[J].中国康复, 2003, (18) pp: 341-343
[43]陈卓铭,尹义臣,莫雷等.汉语失语症计算机辅助流利性检测的研究[J].中华神经医学杂志,2007,(6) pp:317-320
[44]黄锡泉.基于COM组件的VB与MATLAB接口编程[J].电脑编程技巧与维护, 2004, (8) pp: 19-21.
[45]朱志松,郭晓丽,朱晓松. VB与MATLAB混合编程探讨[J].电子技术应用, 2003, (9) pp: 18-19.
[46]谢楠,陈汉良. Visual Basic与Matlab的几种接口编程技术[J].仪器仪表学报,2004, 25(4) pp: 571-574.
[2] Paradis著,林谷辉,林梅溪,陈卓铭译.双语失语症的评估.广州:暨南大学出版社,2003.
[3]唐菱.失语症分类研究概述[J].湖南大学学报, 2003, (17) pp: 100-103.
[4]高素荣.《失语症》[M].北京:北京医科大学、中国协和医科大学联合出版社,1993.
[5]王德春,吴本虎,王德林.《神经语言学》[M].上海:上海外语教育出版社,1997.
[6]罗倩,彭聃龄.失语症的语言学研究综述[J].当代语言学, 2000, 2(4) pp: 248-263.
[7]曹京波,赵纯,金旻,张玉梅.失语症的常用评价方法[J].中国临床康复, 2006, (10) pp:139-141
[8] Akbarzadeh-T, Mohammad-R., Moshtagh-Khorasani, et al. A hierarchical fuzzy rule-based approach to aphasia diagnosis[J]. Journal of Biomedical Informatics. 2007, 40(5) pp: 465-475.
[9] Bongar, Szabolcs. A novel computer-aided neurolinguistic approach to the treatment of aphasia[J]. Period Polytech Electr Eng. 2006, 50(1-2) pp: 115-128
[10] Larsson Inger, Thoren-Jonsson, Anna-Lisa. The Swedish Speech Interpretation Service: An exploratory study of a new communication support provided to people with aphasia[J]. AAC: Augmentative and Alternative Communication.2007, 23(4) pp: 312-322
[11] Masson Veronique, Quiniou Rene. Application of artificial intelligence of aphasia treatment.Proceedings of the 3rd International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. 1991, pp: 907-913.
[12] Moffatt Karyn, McGrenere Joanna, Purves Barbara, et al .The participatory design of a sound and image enhanced daily planner for people with Aphasia.2004 Conference on Human Factors in Computing Systems - Proceedings, CHI.2004, pp: 407-414
[13] Raymer Anastasia, Kohen Francine. Word-retrieval treatment in aphasia: Effects of sentence context[J]. Journal of Rehabilitation Research and Development, 2006, 43(3)pp: 367-377
[14] Weinrich M, Shelton JR, McCall K, et al. Generalization from single sentence to multisentence production in severely aphasic patients[J]. Brain Lang. 1997, (58) pp: 327-352
[15]李巧薇,陈卓铭,黄舜韶.计算机在辅助认知障碍诊断和康复中的应用[J].中国康复理论与实践, 2002, 8(3) pp: 147-148.
[16]何湘智.语音识别的研究与发展,计算机与现代化, 2002, (3)pp: 3-6.
[17]汪洁.失语症的治疗及其进展.实用老年医学,2003, 17(1) pp: 13-15.
[18]陈卓铭,黄舜韶,李巧薇.基于计算机运算特点的语言障碍的分类.中国临床康复.2003,7(7) pp: 1133-1135.
[19]陈卓铭,唐桂华,莫雷等.语言障碍诊治仪ZM2.1的复测信度分析.中国临床康复. 2004, 8(16) pp: 3025-3027.
[20]秦冰,申建国.失语症计算机评定系统的设计与临床应用[J].中国临床康复, 2005, 9 (12)pp:48-49.
[21]王荫华.西方失语症成套测验(WAB)介绍(一)[J].中国康复理论与实践, 1997, (3)pp: 87-89.
[22]王荫华.西方失语症成套测验(WAB)介绍(二)[J].中国康复理论与实践, 1997, (3)pp: 135-140.
[23]陈伶俐,刘晓加.浅谈失语病人的康复训练[J].护理学杂志, 2004, 9(1) pp: 78-80.
[24]柏强.浅析失语症的康复治疗[J].中医药学刊, 2006, 24(4) pp: 707-708.
[25]韩纪庆,张磊,郑铁然等.语音信号处理[M].北京:清华大学出版社, 2005.
[26] Bing-Fei Wu, and Kun-Ching Wang. Robust Endpoint Detection Algorithm Based on the Adaptive Band-Partitioning Spectral Entropy in Adverse Environments. IEEE Trans.on speech and audio processing, 2005, 13(5) pp: 762-775.
[27] C. T. Lin, J. Y. Lin, and G. D. Wu. A robust word boundary detection algorithm for variable noise-level environment in cars. IEEE Trans. Intell.Transp. Syst., 2002, 3(1) pp: 89–101.
[28] Laska AC, Hellblom A, Murray V, et al. Aphasia in acute stroke and relation to outcome. J Intern Med 2001, 24(9) pp: 413–422.
[29] Pedersen PM, Vinter K, Olsen TS.Aphasia after stroke type.severity and prognosis.The Copenhagen aphasia study. Cerebrovasc Dis, 2004, 17(1) pp: 35-43..
[30] Van de, Sandt-KOendeman M, Wiegers J, et al. A computedsed communication aid for people with aphasia[J]. Disabtl Rehabil , 2005, 27(9) pp: 529-533
[31]杨胜跃,周宴宇,黄深喜.语音信号端点检测方法与展望[J].信息技术, 2005, (7)pp: 5-8.
[32]崔冬青,李治柱,吴亚栋.一种噪声环境下连续语音识别的快速端点检测算法[J].计算机工程与应用, 2003, (23) pp: 95-97.
[33]郭巧,张立伟,陆际联.用于汉语语音信号端点检测与切分的有效方法[J].计算机工程与应用. 2000, (5) pp: 92-94.
[34]董力,陈宏钦,马争鸣.基于小波变换的语音段起止端点检测算法[J].中山大学学报(自然科学版), 2005, 44(3) pp: 116-118
[35]赵力.语音信号处理[M].北京,机械工业出版社, 2003.
[36]候雪梅,张雪英,赵高峰.一种改进的基于LP倒谱特征的孤立词语音识别方法[J].太原理工大学学报,2006, (5) pp: 508-510.
[37]陈立伟.基于HMM和ANN的汉语语音识别[D].哈尔滨工程大学博士学位论文.2005
[38]高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社, 2003
[39] Wade J, Petheram B, Cain R.Voice recognition and aphasia: can computer understand aphasic speech. Disabil Rehabil, 2001, 23(14) pp: 604-613
[40] Robert Hecht - Nielson. Theory of the Back - Propagation Neural Network[C]. IJCNN, 1989, 1: 583-604
[41] White H.Commentionist nonparametric regression:Multilayerfeedforward networks can learn arbitrary mapping[J].Neural Networks,1990,(3)
[42]李涛,陈卓铭,尹义臣等.计算机测定失语症语速的相关分析[J].中国康复, 2003, (18) pp: 341-343
[43]陈卓铭,尹义臣,莫雷等.汉语失语症计算机辅助流利性检测的研究[J].中华神经医学杂志,2007,(6) pp:317-320
[44]黄锡泉.基于COM组件的VB与MATLAB接口编程[J].电脑编程技巧与维护, 2004, (8) pp: 19-21.
[45]朱志松,郭晓丽,朱晓松. VB与MATLAB混合编程探讨[J].电子技术应用, 2003, (9) pp: 18-19.
[46]谢楠,陈汉良. Visual Basic与Matlab的几种接口编程技术[J].仪器仪表学报,2004, 25(4) pp: 571-574.