Detection of Hypernasal Speech for Children with Cleft Palate

Akafi, Ehsan; Vali, Mansour; Moradi, Negin

doi:10.22041/ijbme.2012.13118

Document Type : Full Research Paper

Authors

¹ M.S.C, Department of Engineering, Shahed University,

² Assistant professor, Department of Electrical & computer Engineering, K. N. Toosi University of Technology

³ Assistant professor, Speech Therapy Department, Jondishapour University of Medical sciences

10.22041/ijbme.2012.13118

Abstract

Hypernasality is a frequently occurring resonance disorder in children with cleft palate. Generally an operation is necessary to reduce the hypernasality and therefore an assessment of hypernasality is imperative to quantify the effect of the surgery and design the speech therapy sessions which are crucial after surgery. In this study, a new quantitative method is proposed to estimate hypernasality. The proposed method used the fact that an Autoregressive (AR) model for vocal tract system of a patient with hypernasal speech is not accurate; because of the zeros appear in the frequency response of vocal tract system due to existence of extra channel between oral and nasal cavity of these patients. Therefore in our method hypernasality was estimated by a quantity calculated from comparing the distance between the sequences of cepstrum coefficients extracted from AR model and Autoregressive Moving Average (ARMA) model. K-means and Bayes theorem were utilized for finding a threshold value for proposed index to classify the utterances of subjects. We achieved the balanced accuracy up to 82.18% on utterances and 97.72% on subjects. Since the proposed method needs only computer processing of speech data, compare to other clinical methods it is provides a simple evaluation of hypernasality.

Keywords

Main Subjects

Speech processing

References

[1] F. Derakhshandeh, M. Poorjavad, The Study of Speech Disorders and Middle Ear Diseases Following Primary Palatoplasty in Children with Cleft Palate; J. Isfahan Med. Sch., 2011; 29 (130).

[2] D. A. Cairns, J. H. L. Hansen, and J. E. Riski, A noninvasive technique for detecting hypernasal speech using a nonlinear operator; Ieee Trans. Biomed. Eng., 1996; 43(1): 35–45.

[3] “An accelerometric measure as a physical co... [J Speech Hear Res. 1983] - PubMed - NCBI.”

[4] M. Y. Chen, Acoustic parameters of nasalized vowels in hearing-impaired and normal-hearing speakers; J. Acoust. Soc. Am., 1995.

[5] M. A. Redenbaugh and A. R. Reich, Correspondence between an accelerometric nasal/voice amplitude ratio and listeners’ direct magnitude estimations of hypernasality; J. Speech Hear. Res., 1985; 28(2): 273.

[6] G. Fant, Acoustic theory of speech production. Walter de Gruyter, 1970.

[7] S. Hawkins and K. N. Stevens, Acoustic and perceptual correlates of the non-nasal-nasal distinction for vowels; J Acoust Soc Am, 1985; 77(4).

[8] J. Glass and V. Zue, Detection of nasalized vowels in American English in Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP’85., 1985; 10: 1569–1572.

[9] D. A. Cairns, J. H. L. Hansen, and J. F. Kaiser, Recent advances in hypernasal speech detection using the nonlinear teager energy operator in Spoken Language, 1996. ICSLP 96., Fourth International Conference on, 1996; 2: 780–783.

[10] L. R. Rabiner and R. W. Schafer, Digital processing of speech signals, Prentice-hall Englewood Cliffs; NJ, 1978; 100.

[11] D. K. Rah, Y. I. KO, C. Lee, and D. W. Kim, A noninvasive estimation of hypernasality using a linear predictive model; Ann. Biomed. Eng., 2001; 29(7): 587–594.

[12] P. Vijayalakshmi, M. R. Reddy, and D. O’Shaughnessy, Acoustic analysis and detection of hypernasality using a group delay function; Biomed. Eng. IEEE Trans., 2007; 54(4): 621–629.

[13] P. Vijayalakshmi, T. Nagarajan, and V. Jayanthan Ra, Selective pole modification-based technique for the analysis and detection of hypernasality; in TENCON 2009-2009 IEEE Region 10 Conference, 2009, pp. 1–5.

[14] G. S. Lee, C. P. Wang, C. C. H. Yang, and T. B. J. Kuo, Voice low tone to high tone ratio: a potential quantitative index for vowel [a:] and its nasalization; Biomed. Eng. IEEE Trans., 2006; 53(7): 1437–1439.

[15] G. Castellanos, O. D. Castrillón, and E. Guijarro, Multivariate analysis techniques for effective feature selection in voice pathologies. CASEIB, 2004.

[16] K. J. Golding-Kushner, Therapy techniques for cleft palate speech and related disorders. Singular San Diego, 2001.

[17] A. Giovanni, M. Ouaknine, B. Guelfucci, P. Yu, M. Zanaret, and J. M. Triglia, Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds; J. Voice, 1999; 13(4): 465–476.

[18] J. J. Jiang, Y. Zhang, and C. McGilligan, Chaos in voice, from modeling to measurement; J. Voice, 2006; 20(1): 2–17.

[19] باغبان ک، ترابی‌نژاد ف، مرادی ن، بیگلریان الف، بررسی الگوی زمانی خیشومی شدگی در گفتار کودکان فارسی زبان 4 تا 12 ساله با و بدون شکاف کام، مجله پژوهش در علوم توانبخشی، دوره هشتم، شماره سوم،1391.

[20] S. Ha and D. P. Kuehn, Temporal Characteristics of Nasalization in Speakers with and Without Cleft Palate; Cleft Palate. Craniofac. J., 2011; 48(2): 134–144.

[21] A. Gray Jr and J. Markel, A spectral-flatness measure for studying the autocorrelation method of linear prediction of speech analysis; Acoust. Speech Signal Process. Ieee Trans., 1974; 22(3): 207–217.

[22] A. Gray Jr and J. Markel, Distance measures for speech processing; Acoust. Speech Signal Process. Ieee Trans., 1976; 24(5): 380–391.

Iranian Journal of Biomedical Engineering

Detection of Hypernasal Speech for Children with Cleft Palate

References

References

Volume 6, Issue 3 - Serial Number 3
June 2012
Pages 119-129

Detection of Hypernasal Speech for Children with Cleft Palate

References

References

Volume 6, Issue 3 - Serial Number 3June 2012Pages 119-129

Volume 6, Issue 3 - Serial Number 3
June 2012
Pages 119-129