Application of glottal flow descriptors for pathological voice diagnosis
Tóm tắt
Acoustic analysis of speech signal enables automatic detection and classification of voice disorders along with its severity. This automatic assessment provides help to the clinician in initial diagnosis of pathological larynx in non-intrusive way. Voice pathologies damage the vocal cords and consequently alter the dynamics (fluctuation speed) of vocal cords. In this article, we have estimated glottal volume velocity waveform (GVVW) from the speech pressure waveforms of healthy and pathological subjects using quasi closed phase (QCP) glottal inverse filtering algorithm to capture altered dynamics of vocal cords. Closed-phase methods revealed notable stability in diverse voice qualities and sub-glottal pressures. The GVVW is the source of significant acoustical clues rooted in speech. The estimated GVVW is then parameterized by various time based, frequency based and Liljencrants–Fant (LF) model based glottal descriptors. Glottal descriptor’s vectors have been passed on to stochastic gradient descent (SGD) classifier for voice disorder evaluation. The normal pitch utterance of sustained vowel /a/ quarried from German, English, Arabic and Spanish voice databases is used. Information gain (IG) feature scoring technique is employed to select optimal descriptors and to rank them. Several intra and cross-database experiments were performed to explore the usefulness of glottal descriptors for voice disorder detection, severity detection and classification. Student’s t-tests were performed to validate the obtained results.
Tài liệu tham khảo
Airaksinen, M., Raitio, T., Story, B., & Alku, P. (2014). Quasi closed phase glottal inverse filtering analysis with weighted linear prediction. IEEE/ACM Transactions on Audio, Speech, and Language Processing,22(3), 596–607.
Airaksinen, M., Story, B., & Alku, P. (2013). Quasi closed phase analysis for glottal inverse filtering. In Proceedings of the Interspeech 2013, (pp. 143–147).
Airas, M. (2008). TKK Aparat: An environment for voice inverse filtering and parameterization. Logopedics Phoniatrics Vocology,33, 49–64.
Ali, Z., et al. (2017). Intra- and Inter-database study for Arabic, English, and German databases: Do conventional speech features detect voice pathology? Journal of Voice,31(3), 386.e1–386.e8.
Alku, P., Pohjalainen H., & Airaksinen, M. (2017). Aalto Aparat: A freely available tool for glottal inverse filtering and voice source parameterization. In Proceeding of Subsidia: Tools and Resources for Speech Sciences, Malaga.
Al-nasheri, A., Ali, Z., Muhammad, G., & Alsulaiman, M. (2014). Voice pathology detection using auto-correlation of different filters bank. Proceedings of 11th ACS/IEEE International Conference on Computer Systems and Applications, (pp. 110–117).
Al-nasheri, A., et al. (2016). An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification. Journal of Voice,31(1), 1139.e9–113.e18.
Al-nasheri, A., et al. (2018). Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions. IEEE Access,6, 6961–6974.
Arias-Londõno, J., Godino-Llorente, J., Markaki, M., & Stylianou, Y. (2011). On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices. Logopedics Phoniatrics Vocology,36, 60–69.
Arjmandi, M., Pooyan, M., Mikaili, M., Vali, M., & Moqarehzadeh, A. (2011). Identification of voice disorders using long-time features and support vector machine with different feature reduction methods. Journal of Voice,25(6), 275–289.
Barry, W., & Pützer, M. (2017). Saarbrucken voice database. Institute of Phonetics. Retrieved May 2, 2017 from https://www.Stimmdatenbank.coli.uni-saarland.de.
Benmalek, E., Elmhamdi, J., & Jilbab, A. (2018). Multiclass classification of Parkinson’s disease using cepstral analysis. International Journal of Speech Technology,21(1), 39–49.
Boyanov, B., & Hadjitodorov, S. (1997). Acoustic analysis of pathological voices. IEEE Engineering in Medicine and Biology Magazine,16, 74–82.
Boyanov, B., Ivanov, T., Hadjitodorov, S., & Chollet, G. (1993). Robust hybrid pitch detector. Electronics Letters,29(22), 1924–1926.
Davis, S. (1979). Acoustic characteristics of normal and pathological voices. Speech and Language,1, 271–335.
Drugman, T., Bozkurt, B., & Dutoit, T. (2012). A comparative study of glottal source estimation techniques. Computer Speech & Language,26(1), 20–34.
Fant, G., Liljencrants, J., & Lin, Q. (1985). A four-parameter model of glottal flow. STL-QPSR,26(4), 001–013.
Fontes, A., Souza, P., Neto, A., Martins, A., & Silveira, L. (2014). Classification system of pathological voices using correntropy. Mathematical Problems in Engineering. https://doi.org/10.1155/2014/924786.
Godino-Llorente, J., & Gómez-Vilda, P. (2004). Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. IEEE Transactions on Biomedical Engineering,51(2), 380–384.
Godino-Llorente, J., Gómez-Vilda, P., & Blanco-Velasco, M. (2006). Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Transactions on Biomedical Engineering,53(10), 1943–1953.
Kasuya, H., Ogawa, S., Mashima, K., & Ebihara, S. (1986). Normalized noise energy as an acoustic measure to evaluate pathologic voice. Journal of the Acoustical Society of America,80(5), 1329–1334.
Kay Elemetrics Corp. (1994). Disordered voice database, Version 1.03 (CD-ROM), MEEI, Voice and Speech Lab. Boston: Kay Elemetrics Corp.
Lee, J., Kang, H., & Choi, J. (2013). An investigation of vocal tract characteristics for acoustic discrimination of pathological voices. BioMed Research International,2013, 1–11.
Lehto, L., Airas, M., Björkner, E., Sundberg, J., & Alku, P. (2007). Comparison of two inverse filtering methods in parameterization of the glottal closing phase characteristics in different phonation types. Journal of Voice,21(2), 138–150.
Leonardo, A., Kohler, M., Vellasco, M., & Cataldo, E. (2015). Analysis and classification of voice pathologies using glottal signal parameters. Journal of Voice,30(5), 549–556.
Ma, C., Kamp, Y., & Willems, L. (1993). Robust signal selection for linear prediction analysis of voiced speech. Speech Communication,12(1), 69–81.
Manfredi, C., Pierazzi, L., & Bruscaglioni, P. (1999). Pitch estimation for noise retrieval in time and frequency domain. Medical & Biological Engineering & Computing,37(2), 532–533.
Markaki, M., & Stylianou, Y. (2011). Voice pathology detection and discrimination based on modulation spectral features. IEEE Transactions on Audio Speech and Language Processing,19(7), 1938–1948.
Mesallam, T., et al. (2017). Development of the Arabic voice pathology database (AVPD) and its evaluation by using speech features and machine learning algorithms. Journal of Healthcare Engineering,8, 1–13.
Michaelis, D., Gramss, H., & Strube, W. (1997). Glottal-to-Noise ratio: A new measure for describing pathological voices. Acustica/Acta Acustica,83, 700–706.
Muhammad, G., & Melhem, M. (2014). Pathological voice detection and binary classification using MPEG-7 audio features. Biomedical Signal Processing and Control,11, 1–9.
Muhammad, G., et al. (2017). Voice pathology detection using interlaced derivative pattern on glottal source excitation. Biomedical Signal Processing and Control,31, 156–164.
Nemr, K., et al. (2012). GRBAS and Cape-V scales: High reliability and consensus when applied at different times. Journal of Voice,26(6), 812.e17–822.e17.
Panek, D., Skalski, A., & Gajda, J. (2014). Quantification of linear and non-linear acoustic analysis applied to voice pathology detection. Information Technologies in Biomedicine,4, 355–364.
Qi, Y., & Hillman, R. (1997). Temporal and spectral estimations of harmonics-to-noise ratio in human voice signals. Journal of the Acoustical Society of America,102(1), 537–543.
Rose, P., & Robertson, J. (2002). Forensic speaker identification. London: Taylor & Francis.
Sauder, C., Bretl, M., & Eadie, T. (2017). Predicting voice disorder status from smoothed measures of cepstral peak prominence using PRAAT and analysis of dysphonia in speech and voice. Journal of Voice,31(5), 557–566.
Sousa, R., Ferreira, A., & Alku, P. (2014). The harmonic and noise information of the glottal pulses in speech. Biomedical Signal Processing and Control,10, 137–143.
Szaleniec, J., Modrzejewski, M., Szaleniec, M., & Wszolek, W. (2007). Application of new acoustic parameters in ANN-aided pathological speech diagnosis. Archives of Acoustics,32(1), 177–186.
Tulics, M., & Vicsi, K. (2019). The automatic assessment of the severity of dysphonia. International Journal of Speech Technology,22(1), 1–10.
Winholtz, W. (1992). Vocal tremor analysis with the vocal demodulator. Journal of Speech and Hearing Research,35(3), 562–573.
Wszolek, W. (2006). Selected methods of pathological speech signal analysis. Archives of Acoustics,31(4), 413–430.
Yumoto, E., Sasaki, Y., & Okamura, H. (1984). Harmonics-to-noise ratio and psychophysical measurement of the degree of hoarseness. Journal of Speech and Hearing Research,27(1), 2–6.