Computer Science ›› 2020, Vol. 47 ›› Issue (1): 144-152.doi: 10.11896/jsjkx.180701349

Special Issue: Medical Imaging

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Automatic Detection Algorithm of Pharyngeal Fricative in Cleft Palate Speech Based on Multi-delay Fourth-order Cumulant Octave Spectral Line

HE Fei1,MENG Yu-xuan1,TIAN Wei-wei1,WANG Xi-yue1,HE Ling1,YIN Heng2   

  1. (School of Electrical Engineering and Information,Sichuan University,Chengdu 610065,China)1;
    (State Key Laboratory of Oral Diseases,Chengdu 610041,China)2
  • Received:2018-07-21 Published:2020-01-19
  • About author:HE Fei,born in 1998,postgraduate.Her main research interests include speech signal processing and image processing;YIN Heng,born in 1971,master.Her main research interests include evaluation of cleft palate speech.
  • Supported by:
    This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (61503264).

Abstract: In order to realize the automatic classification and detection of palate pharyngeal fricative and normal speech, an automatic pharyngeal fricative detection algorithm based on multi-delay fourth-order cumulant one-third octave spectral line (FTSL) was proposed by studying the pronunciation characteristics of cleft palate patients with pharyngeal fricative.Currently,most researches involved with the detection of pharyngeal fricatives are based on the length of consonants and the energy distribution of speech in frequency-domain.There exist few researches which have achieved automatic classification of pharyngeal fricatives and normal speech.This experiment is based on the pronunciation characteristics of pharyngeal fricative.Each frame’s multi-delay fourth-ordercumulant is computed,and then one-third octave is used to extract the FTSL.Automatic classification of pharyngeal fricative and normal speech is realized by FTSL.In this experiment,the FTSL of 200 normal consonants and 194 consonants of pharyngeal fricative are extracted,and the SVM classifier is used to classify.Besides,comparative experiments were conducted on FTSL feature and traditional acoustic features,and the results were fully analyzed and discussed in this paper.The experimental results show that the proposed FTSL has an accurate rate of 92.7% for the automatic classification of pharyngeal speeches,and it has excellent performance and can provide an effective,objective and non-invasive auxiliary basis for clinical pharyngeal state assessment.

Key words: Fourth-order cumulant, FTSL spectral line, One-third octave spectral line, Pharyngeal fricative

CLC Number: 

  • TP391.9
[1]XIAO Y,LIANG M G.Automatic Detection ofPharyngealFricativesin Cleft Palate Speech[C]∥Proceedings of the 4th International Conferenceon Computer Engineering and Networks.Springer International Publishing.2015:861-868.
[2]REN Z,ZHOU X,MA L,et al.Comparison Study of Vocal Attack Time in Patients With Cleft Palate With and Without Glottal Stop in Mandarin[J].Journal of Voice:Official Journal of the Voice Foundation,2018,33(5):803.e15-803.e21.
[3]MA S W,REN Z P,WEN Y X,et al.Compensatory articulation in patients with repaired cleft palate and the speech therapy approach[J].Journal of Practical Stomatology,2012,28(5):619-622.
[4]GUERRA T A,MARINO V C C,ROCHA D C,et al.Nasalance at presence and absence of pharyngeal fricative[J].Speech,Language,Hearing Sciences and Education Journal,2016,18(2):449-458.
[5]DENG S H,WANG T S,HUANG R C,et al.Study on the characteristics of the speech of persons with disorder and sound after Postoperatively in cleft palate[J].China Prac Med,2017,12(2):194-195.
[6]JIANG L P,WANG G M,YANG Y S,et al.The study on articulation characteristics of the patients after pharyngoplasty[J].China Journal of Oral Maxillofacial Surgery,2005(1):56-58.
[7]MA L.The acoustic features and the articulation character of tougue movement of pharyngeal fricatives ∥Abstracts of the 2nd Chinese International Congress on Oral and Maxillofacial Surgery in Conjunction with 5th National Congress on Oral and Maxillofacial Surgery.1998:267-268.
[8]GARCIA A F,MARINO V C,PEGORARO-KROOK M I,et al.Nasalance during use of pharyngeal and glottal place of production[J].Codas,2014,26(5):395-401.
[9]WANG G M,CHEN Y,QIU W L,et al.Clinical application and evaluation in analysis of articulation disorders WTH TSL[J].J. Oral Maxil. Surg.,2000(3):189-197.
[10]ZHANG C H,ZHOU H Y,JIAO X H.Phonetic fbatIlres of before and after posterior pharyngeal flap surgery in older parents with velopharyngeal insufficiency[J].Journal of Harbin Medical University,2016,50(2):162-165.
[11]ZHU Y S,SHI J J.A acoustic technology analysis of misarticulation in patients with cleft palate [J].Journal of Practical Stomatology,2004(3):364-366.
[12]ALAM M K,ZULKIPLI A S,HAQUE S,et al.A perceptual evaluation of speech disorders in children with repaired unilateral cleft lip and palate in Hospital UniversitiSains Malaysia[J].Angladesh Journal of Medical Science,2018,17(3):470-478.
[13]MCLEOD S, CROWE K.Children’s Consonant Acquisition in 27 Languages:A Cross-Linguistic Review[J].Am. J. Speech Lang Pathol.,2018,27:1546-1571.
[14]雷丽.腭裂语音治疗学[M].武汉:湖北科学技术出版社,2004:24-37.
[15]TROST J E.Articulatory additions to the classical description of the speech of persons with cleft palate[J].Cleft Palate Journal,1981,18(3):193-203.
[16]张贤达.时间序列分析一高阶统计量方法[M].北京:清华大学出版社,1999.
[17]张贤达.现代信号处理(2版)[M].北京:清华大学出版社,2002.
[18]DONG X H.Application of MUSIC algorithm based on fourth-order cumulants in high frequency ground wave radar[D].Wuhan:Wuhan University,2004.
[19]FAN Y Y.High order statistics feature extraction of ship noise and its response[D].Xi’an:Northwestern Polytechnical University,1999.
[20]VOSOUGHI E,JAVAHERIAN A.Parameters effective on estimating a nonstationary mixed-phase wavelet using cumulant matching approach[J].Journal of Applied Geophysics,2018,148:83-97.
[21]LV J Y.High Order Statistics Analysis and its Applications
[D].Beijing:Beijing University of Posts and Telecommunications,2014.
[22]ANANTHRAM S,GEORGIOS B,et al.Bibliography on higher-order statistics[J].Signal Processing,1997(60):65-66.
[23]MENDEL J M.Tutorial on higher order statistics (spectra) in signal processing and system theory:Theoretical results and some applications[J].Proc.IEEE,1991,79(3):278-305.
[24]ALBATAINEH Z.Robust blind channel estimation algorithm for linear STBC systems using fourth ordercumulant matrices[J].Telecommunication Systems,2018,68(3):573-582.
[25]LIANG H,YANG C S.A Signal DetectionAlgorithm Based on Fourth-orderCumulant[J].Torpedo Technology,2007(5):48-50.
[26]MEI T M.Blind signal separation algorithm based on symmetric fourth-order mutual cumulant[C]∥Signal Processing Branch of China Electronics Society and Signal Processing Branch of China Institute of Instruments and Instruments.2003:4.
[27]ELIAS N,RAFIK G,SAMY M.Speechenhancement using fourth-order cumulants and optimum filters in the subband do- main[J].Speech Communication,2002,36(3):219-246.
[28]QIAN Z,LI X Y,ZHANG R B,et al.Speech-stream detection in short-wave channel based on empirical mode decomposition and higher-order statistics[J].Journal of Harbin Institute of Technology,2009,16(5):713-716.
[29]BAO H Q.A brief introduction to phonological and acoustic analysis of Putonghua (continued 1)[J].Journal of Audiology and Speech Pathology,2004(4):285-286.
[30]CHENG J,LI G H,ZHOU G L.Simplified Calculating Simulation of Fourth-Order Cumulants[J].Computer Simulation,2009,26(8):80-83.
[31]ZHANG A Q,ZHANG X H.Recursive estimation of fourth-order cumulants and application[J].Signal Processing,2002(1):88-90.
[32]LU W C,CHEN N Y,YE C Z,et al.Introduction to Support Vector Machine Algorithms and Software ChemSVM[J].Computer and Applied Chemistry,2002(6):697-702.
[33]FAN X W.Research and application of support vector machine algorithm [D].Hangzhou:Zhejiang University,2003.
[34]QIN Y Q,ZHANG X Y.Speech signal emotion recognition based on SVM[J].Journal of Circuits and Systems,2012,17(5):55-59.
[35]NAZEER O,JAVAID N,et al.Short Term Load Forcasting Using Heuristic Algorithm and Support Vector Machine[C]∥12th International Conference on Complex,Intelligent,and Software Intensive Systems (CISIS).2019:791-799.
[36]LUO R L.Research on text independent speaker recognition algorithm based on SVM[D].Lanzhou:Lanzhou University of Technology,2009.
[37]LUO R L.Text independent speaker recognition algorithm based on SVM[D].Lanzhou:Lanzhou University of Technology,2009.
[38]TANG J T,HU D,GONG Z M.Research on image texture classification based on SVM[J].Computer Engineering and Science,2008(8):44-45,48.
[39]GANDEK B,WARE J E,AARONSON N K,et al.Cross-validation of item selection and scoringfor the SF-12 Health Survey in nine countries:results from the IQOLA Project[J].Journal of clinical epidemiology,1998,51(11):1171-1178.
[1] CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75.
[2] WANG Jin, LIU Jiang. GPU-based Parallel DILU Preconditioning Technique [J]. Computer Science, 2022, 49(6): 108-118.
[3] SHAO Xin-xin. TI-FastText Automatic Goods Classification Algorithm [J]. Computer Science, 2022, 49(6A): 206-210.
[4] MAO Sen-lin, XIA Zhen, GENG Xin-yu, CHEN Jian-hui, JIANG Hong-xia. FCM Algorithm Based on Density Sensitive Distance and Fuzzy Partition [J]. Computer Science, 2022, 49(6A): 285-290.
[5] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
[6] MAO Dian-hui, HUANG Hui-yu, ZHAO Shuang. Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance [J]. Computer Science, 2022, 49(6A): 523-530.
[7] ZHOU Chu-lin, CHEN Jing-dong, HUANG Fan. WiFi-PDR Fusion Indoor Positioning Technology Based on Unscented Particle Filter [J]. Computer Science, 2022, 49(6A): 606-611.
[8] XU Jia-nan, ZHANG Tian-rui, ZHAO Wei-bo, JIA Ze-xuan. Study on Improved BP Wavelet Neural Network for Supply Chain Risk Assessment [J]. Computer Science, 2022, 49(6A): 654-660.
[9] CHEN Yu-si, AI Zhi-hua, ZHANG Qing-hua. Efficient Neighborhood Covering Model Based on Triangle Inequality Checkand Local Strategy [J]. Computer Science, 2022, 49(5): 152-158.
[10] ZHAO Geng, WANG Chao, MA Ying-jie. Study on PAPR Reduction Based on Correlation of Chaotic Sequences [J]. Computer Science, 2022, 49(5): 250-255.
[11] LIN Jin-cheng, JI Qing-ge, ZHONG Zhen-wei. Modified Social Force Model Considering Pedestrian Characteristics and Leaders [J]. Computer Science, 2022, 49(5): 347-354.
[12] JIANG Hua-nan, ZHANG Shuai, LIN Yu-fei, LI Hao. Simulation Optimization and Testing Based on Gazebo of MPI Distributed Parallelism [J]. Computer Science, 2021, 48(11A): 672-677.
[13] SHAO Xin-xin. Service Recommendation Algorithm Based on Canopy and Shared Nearest Neighbor [J]. Computer Science, 2020, 47(11A): 479-481.
[14] CHEN Pei, ZHENG Wan-bo, LIU Wen-qi, XIAO Min, ZHANG Ling-xiao. Analysis and Forecast of Some Climate Indexes in Main Producing Areas of Yunnan Province Based on Multiple Models [J]. Computer Science, 2020, 47(11A): 496-503.
[15] ZENG Lei, LI Hao, LIN Yu-fei, ZHANG Shuai. Study on Simulation Optimization of Gazebo Based on Asynchronous Mechanism [J]. Computer Science, 2020, 47(11A): 593-598.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!