Total views : 2042

A Comparative Study of Recognition Technique Used for Development of Automatic Stuttered Speech Dysfluency Recognition System

Affiliations

  • Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad – 431004, Maharashtra, India
  • Department of Computer Science, Vivekanand College, Tarabai Park, Kolhapur – 416003, Maharashtra, India
  • MGM’s Institute of Biosciences and Technology, Aurangabad – 413003, Maharashtra, India
  • Department of Computer Science and Engineering, Maharashtra Institute of Technology, Aurangabad – 411038, Maharashtra, India

Abstract


Objectives: This paper is an attempt to compare the work done around the world for development of stuttered speech database and approaches for analysis of stuttered speech and recognition system. Methods/Statistical Analysis: In particular we have compared the different methods adopted by the researchers around the world for development of speech database and the techniques implemented on these developed databases. We have compared the databases on the basis of utterances, gender, age group, speech dysfluencies and type of samples. The recognition systems are compared on the basis of feature used, classification techniques and the accuracy. Findings: Speech recognition based application is getting more popularized and now being implemented at various places. However, the developed speech recognition systems cannot handle the speech dysfluencies. Very less work had been carried out till date for stuttered speech recognition system. The work for Indian languages is very negligible. The only work carried out is for Kannada. There is no major contribution for other Indian Languages. This paper shows the current status and the notable work carried in other languages. Application/Improvements: There is a need to develop more such systems for other Indian languages which will be very helpful for multilingual society like India.

Keywords

Stuttered Speech, Stuttered Speech Dysfluency Recognition System, Speech Dysfluency, Stuttered Speech Database

Full Text:

 |  (PDF views: 252)

References


  • Awad S. The application of digital speech processing to stuttering therapy. Proceedings of Instrumentation and Measurement Technology Conference Sensing Processing Networking; 1997. p. 1361–7. Crossref
  • Borsel JV, Achten E, Santens P, Lahorte P, VoetT. FMRI of developmental stuttering a pilot study. Journal of Brain and language. 2003; 85(3):369–76.
  • Bloodstein O. A handbook on stuttering. Chicago the National Easter Seal Society; 1987.
  • Waghmare VB, Deshmukh RR, Shrishrimal PP, Janvale GB. Emotion recognition system from artificial marathi speech using mfcc and ldatechniques. Proceeding of 5th International Conference on Advances in Communication Network and Computing–CNC; 2014. p. 1–9.PMCid:PMC4090972
  • Landge MB, Deshmukh RR, Shrishrimal PP. Analysis of variations in speech in different age groups using prosody technique. International Journal of Computer Applications.2015; 126(1):1–4.
  • Guntupalli VK, Kalinowski J, Saltuklaroglu T. The need for self‐report data in the assessment of stuttering therapy efficacy repetitions and prolongations of speech. The stuttering syndrome. International Journal of Language and Communication Disorders. 2006; 41(1):1–18.PMid:16272000. Crossref
  • Fisher SE, Khadem FV, WatkinsKE, Monaco AP, Pembrey ME. Localisation of a gene implicated in a severe speech and language disorder. Nature Genetics. 1998; 18:168–70.PMid:9462748. Crossref
  • American speech-language-hearing association. Scope of Practice in Speech-Language Pathology; 2007.
  • Skljarov O, Bortnik T. Chaos and speech rhythm.Proceedings of IEEE International Joint Conference on Neural Networks, IJCNN’05; 2007. p. 2070–5.
  • Tan TS, Ariff AK, Ting CM, Salleh SH. Application of malay speech technology in malay speech therapy assistance tools. Proceeding of International Conference on Intelligent and Advanced Systems IEEE-ICIAS; 2007. p. 330–4. Crossref
  • Geetha YV, Pratibha K, Ashok R, Ravindra SK. Classification of childhood disfluencies using neural networks. Journal of Fluency Disorders. 2000; 25(2):99–117. Crossref
  • World Health Organization. Available from: Crossref
  • Dewey D. What is developmental dyspraxia. Journal of Brain and Cognition. 1995; 29(3):254–74. PMid:8838385.Crossref
  • World Health Organization. International Statistical Classification of Diseases and Related Health Problems; 2004.
  • Świetlicka I, Jóźkowiak WK, SmołkaE. Hierarchical ANN system for stuttering identification. Journal of Computer Speech and Language. 2013; 27(1):228–42. Crossref
  • Szabelska E, Kruczyńska A. Computer-based speech analysis in stutter. Applied Computer Science. 2013; 9(2):34–42.
  • Czyżewski A, Kostek B, Skarżyński H. Technika komputerowa w audiologiifoniatriiilogopedii. Akademicka Oficyna Wydawnicza Exit; 2002.
  • Chen WY, Chen SH, Lin CJ. A speech recognition method based on the sequential multi-layer perceptrons. Journal of Neural Networks. 1996; 9(4):655–69. Crossref
  • Shriberg EE. Phonetic consequences of speech disfluency. Sri International Menlo Park CA; 1999.
  • Shrishrimal PP, Deshmukh RR, Waghmare VB. Development of isolated words speech database of marathi words for agriculture purpose. Asian Journal of Computer Science and Information Technology. 2013; 2(7):217–8.
  • Ward D. Sudden onset stuttering in an adult: Neurogenic and psychogenic perspectives. Journal of Neurolinguistics.2010; 23(5):511–7. Crossref
  • Krishnan G, Tiwari S. Revisiting the acquired neurogenic stuttering in the light of developmental stuttering. Journal of Neurolinguistics. 2011; 24(3):383–96. Crossref
  • Ravikumar K, Reddy B, Rajagopal R, Nagaraj H. Automatic detection of syllable repetition in read speech for objective assessment of stuttered disfluencies. Proceedings of World Academy Science Engineering and Technology. 2008; 36:270–73.
  • Subramanian A, Yairi E. Identification of traits associated with stuttering. Journal of Communication Disorders.
  • ; 39(3):200–16. PMid:16455103. Crossref
  • Howell P, Sackin S, Glenn K. Development of a two-stage procedure for the automatic recognition of dysfluencies in the speech of children who stutter. Psychometric procedures appropriate for selection of training material for lexical dysfluency classifiers. Journal of Speech, Language, and Hearing Research. 1997; 40(5):1073–84. PMid:9328878 PMCid:PMC2000472. Crossref
  • Ravikumar KM, Rajagopal R, Nagaraj HC. An approach for objective assessment of stuttered speech using MFCC features. ICGST International Journal on Digital Signal Processing DSP. 2009; 9:19–24.
  • Howell P, Sackin S, Yeung JA. Assessment procedures for locating stuttered events. Proceedings of the 2nd World Congress on Fluency Disorders; 1998.
  • Jacqueline S. Speaking easy for kids who stutter early treatment can make the words flow smoothly. Journal of Health.1991.
  • Archibald L, De Nil LF. The relationship between stuttering severity and kinesthetic acuity for jaw movements in adults who stutter. Journal of Fluency Disorders. 1999; 24(1):25– 42. Crossref
  • Howell P, Yeung JA, Sackin S. Exchange of stuttering from function words to content words with age. Journal of Speech Language and Hearing Research. 1999; 42(2):345– 54. PMid:10229451 PMCid:PMC2013932. Crossref
  • Awad SS, Coreless MW, Merson R. Computer assisted treated for motor speech disorders.. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference; 1999. p. 595–600. Crossref
  • Nöth E, Niemann H, Haderlein T, Decher M, Eysholdt U, Rosanowski F, Wittenberg T. Automatic stuttering recognition using hidden Markov models. Proceeding of 6th International Conference on Spoken Language Processing.2000; 4:65–8.
  • Hollingshead K, Heeman P. Using a uniform-weight grammar to model disfluencies in stuttered read speech a pilot study. Center for Spoken Language Understanding; 2004.p. 1–22. PMid:15196814.
  • Lincoln M, Packman A, Onslow M. Altered auditory feedback and the treatment of stuttering- A review. Journal of Fluency Disorders. 2006; 31(2):71–89. PMid:16750562. Crossref
  • Voigt T, Hewage K, Alm P. Smartphone support for persons who stutter. Proceedings of the 13th International Symposium on Information Processing in Sensor Networks IEEE Press; 2014. p. 293–94.
  • Miyamoto C, Komai Y, Takiguchi T, Ariki Y, Li I. Multimodal speech recognition of a person with articulation disorders using AAM and MAF. IEEE International Workshop on Multimedia Signal Processing (MMSP); 2010. p. 517–20.Crossref
  • Ai OC, Yunus J. Overview of a computer-based stuttering therapy. Regional Postgraduate Conference on Engineering and Science; 2006. p. 207–11.
  • Jóźkowiak WK, Smołka E, Adamczyk B. Effect of acoustical visual and tactile echo on speech fluency of stutterers. Folia phoniatrica et logopaedica. 1996; 48(4):193–200. Crossref
  • Awad SS, Piechocki C. Speech therapy software on an open web platform. 10th IEEE Computer Engineering Conference (ICENCO); 2014. p. 53–6. Crossref
  • Awa SS, Corless MW, Przebienda L, Merson R. Development of a computer based speech fluency treatment aid. The Proceedings of the 4th IEEE International Conference on Electronics Circuits and Systems ICECS97; 1997. p. 245–8.
  • Blomgren M, Roy N, Callister T, Merrill RM. Intensive stuttering modification therapy a multidimensional assessment of treatment outcomes. Journal of Speech, Language, and Hearing Research. 2005; 48(3):509–23. Crossref
  • Guitar B, Peters TJ. Stuttering- An Integration of Contemporary Therapies; 1980.
  • Sidavi A, Fabus R. A review of stuttering intervention approaches for preschool-age and elementary school-age children. Contemporary Issues in Communication Science Disorders. 2010; 37:14–26.
  • Howell P. Assessment of some contemporary theories of stuttering that apply to spontaneous speech. Contemporary Issues in Communication Science and Disorders CICSD. 2004; 31:122–39. PMid:18259590 PMCid:PMC2231590.
  • Howell P, Sackin S. Automatic recognition of repetitions and prolongations in stuttered speech. Proceedings of the 1st World Congress on Fluency Disorders. 1995; 2:372–4.
  • Howell P, Sackin S, Glenn K. Development of a two-stage procedure for the automatic recognition of dysfluencies in the speech of children who stutterI. Psychometric procedures appropriate for selection of training material forlexical dysfluency classifiers. Journal of Speech, Language, and Hearing Research. 1997; 40(5):1073–84. PMid:9328878 PMCid:PMC2000472. Crossref
  • Ramaboka M, Manamela J, Gasela N. Automatic Speech Recognition for People with Speech Disorders; 2012. p. 1–2.
  • Yeung JA, Gomez IV, Howell P. Exchange of disfluency with age from function words to content words in Spanish speakers who stutter. Journal of Speech Language and Hearing Research. 2003; 46(3):754–65. Crossref
  • Dworzynski K, Howell P. Predicting stuttering from phonetic complexity in German. Journal of Fluency Disorders. 2004; 29(2):149–73. PMid:15178130. Crossref
  • Zebrowski PM. Duration of the speech disfluencies of beginning stutterers. Journal of Speech Language and Hearing Research. 1991; 34(3):483–91. Crossref
  • Szczurowska I, Jozkowiak WK, Smolka E. The application of Kohonen and Multilayer Perceptron Networks in the speech nonfluency analysis. Archives of Acoustics. 2014; 31(4):205–10.
  • Wisniewski M, Jozkowiak WK, Smolka E, Suszynski W. Automatic detection of disorders in a continuous speech with the hidden Markov models approach. Proceeding of Computer Recognition Systems Springer Berlin Heidelberg. 2007; 2:445–53. Crossref
  • Wisniewski M, Jozkowiak WK, Smolka E, Suszynski W. Automatic detection of prolonged fricative phonemes with the hidden Markov models approach. Journal of Medical Informatics and Technologies. 2007.
  • Czyzewski A, Kaczmarek A, Kostek B. Intelligent processing of stuttered speech. Journal of Intelligent Information Systems. 2003; 21(2):143–71. Crossref
  • Szczurowska I, Jozkowiak WK, Smolka E. Speech nonfluency detection using Kohonen networks. Neural
  • Computing and Applications. 2009; 18(7):677–87. Crossref
  • Prakash B. Acoustic measures in the speech of children with stuttering and normal non-fluency-a key to differential diagnosis. Workshop on Spoken Language Processing; 2003.
  • Johnson MH, Gunderson J, Penman A, Huang T. HMMbased and SVM-based recognition of the speech of talkers with spastic dysarthria. IEEE International Conference on Acoustics Speech and Signal Processing ICASSP 2006 Proceedings. 2006; 3:1–4. Crossref
  • Swietlicka I, Jozkowiak WK, SmolkaE. Artificial neural networks in the disabled speech analysis. Springer Berlin Heidelberg. Proceeding of Computer Recognition Systems. 2009; 3:347–54. Crossref
  • Poroshin AN, Jarov OP. An internet system of partnerlearning special type. In International Conference on
  • Physics and Control. 2003; 2:703–6. Crossref
  • Chee LS, Ai OC, Hariharan M, Yaacob S. MFCC based recognition of repetitions and prolongations in stuttered speech using k-NN and LDA. IEEE Student Conference on Research and Development (SCOReD); 2009. p. 146–9.Crossref
  • Chee LS, Ai OC, Hariharan M, Yaacob S. Automatic detection of prolongations and repetitions using LPCC.
  • International Conference for Technical Postgraduates (TECHPOS); 2009. p. 1–4. Crossref
  • Wisniewski M, Jozkowiak WK, Smolka E, Suszynski W. Automatic detection of prolonged fricative phonemes with the hidden Markov models approach. Journal of Medical Informatics and Technologies. 2007.
  • Bergl P, Lustyk T, Cmejla R, Cerny L, Hrbkova M. Assessment of dysfluency in stuttered speech. Proceeding of Technical Computing of Bratislava; 2010. p. 1–3.
  • Fook CY, Muthusamy H, Chee LS, Yaacob SB, Adom AHB. Comparison of speech parameterization techniques for the classification of speech disfluencies. Turkish Journal of Electrical Engineering and Computer Sciences. 2013; 21(1):1983–94. Crossref
  • Pálfy J. Analysis of dysfluencies by computational intelligence. Information Sciences and Technologies. 2014;
  • (2):1–14.
  • Yeh PH, Yang SL, Yang CC, Shieh MD. Automatic Recognition of repetitions in stuttered speech using endpoint detection and dynamic time warping. Proceeding of Social and Behavioral Sciences. 2015; 193:356. Crossref
  • Mahesha P, Vinod DS. Gaussian mixture model based classification of stuttering dysfluencies. Journal of Intelligent Systems. 2016; 25(3):387–99. Crossref
  • Ai OC, Hariharan M, Yaacob S, Chee LS. Classification of speech dysfluencies with MFCC and LPCC features. Journal of Expert Systems with Applications. 2012; 39(2):2157–65. Crossref
  • Hariharan M, Chee LS, Ai OC, Yaacob S. Classification of speech dysfluencies using LPC based parameterization techniques. Journal of Medical Systems. 2012; 36(3):1821– 30. PMid:21249515. Crossref
  • Wingate EM, Howell P. Foundations of stuttering. The Journal of the Acoustical Society of America. 2002; 112(4):1229–31. Crossref
  • Cordes AK, Ingham RJ, Frank P, Ingham JC. Time-interval analysis of interjudge and intrajudge agreement for stuttering event judgments. Journal of Speech Language and Hearing Research. 1992; 35(3):483–94. Crossref
  • Kully D, Boberg E. An investigation of interclinic agreement in the identification of fluent and stuttered syllables. Journal of Fluency Disorders. 1988; 13(5):309–18. Crossref
  • Palfy J, Pospichal J. Pattern search in dysfluent speech. Proceeding of International Workshop on Machine Learning for Signal Processing (MLSP); 2012. p. 1–6. Crossref

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.