Summary
Objective
Method
Results
Conclusion
Key Words
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of VoiceREFERENCES
- Review: occupational risks for voice problems.Logoped Phoniatr Vocol. 2001; 26: 37-46
- Recommended protocols for instrumental assessment of voice: American speech-language-hearing association expert panel to develop a protocol for instrumental assessment of vocal function.Am J Speech Lang Pathol. 2018; 27: 887-905
- Evidence-based clinical voice assessment: a systematic review.Am J Speech Lang Pathol. 2013; 22: 212-226
- Beyond the Buzzwords: artificial Intelligence in Laryngology.J Voice. 2022; 36 (Available at:) (Accessed May 12, 2022): 2-3
Lopes L, Cavalcante D, CoDAS PC. Severity of voice disorders: integration of perceptual and acoustic data in dysphonic patients. SciELO Brasil. 2014. Available at: https://www.scielo.br/j/codas/a/kGTm3ryX49stcPVt9YvC5vS/abstract/?lang=en. Accessed February 6, 2022.
Lopes L, Simões L, Voice J da SJ. Accuracy of acoustic analysis measurements in the evaluation of patients with different laryngeal diagnoses. Elsevier. 2017. Available at: https://www.sciencedirect.com/science/article/pii/S0892199716301588. Accessed February 6, 2022.
Lopes L, Vieira V, Costa S, et al. Effectiveness of recurrence quantification measures in discriminating subjects with and without voice disorders. Elsevier. 2020. Available at: https://www.sciencedirect.com/science/article/pii/S0892199718303448?casa_token=l3factj6UCEAAAAA:9ZyDPtjY6T_FZaAZIAel9LYgTyWZCk2nUFkNEO_wcVwpO1hGFA3QgXQMRt_DGpZevK5nao7Q. Accessed May 12, 2022.
- Accuracy of acoustic analysis measurements in the evaluation of patients with different laryngeal diagnoses.J Voice. 2017; 31: 382.e15-382.e26
Stuart Russell and Peter Norvig - Artificial intelligence: a modern approach. 3rd ed. Available at: https://www.academia.edu/download/61853459/Artificial-Intelligence-A-Modern-Approach-3rd-Edition-by-Stuart-Russell-Peter-Norvig20200121-107745-13gd7bj.pdf. Accessed July 3, 2022.
Jo T. Machine learning foundations. 2021. Available at: https://link.springer.com/content/pdf/10.1007/978-3-030-65900-4.pdf. Accessed July 3, 2022.
- Machine learning: trends, perspectives, and prospects.Science. 1979; 349: 255-260
Mitchell T, Mitchell T. Machine learning. 1997. Available at: https://profs.info.uaic.ro/∼ciortuz/SLIDES/2017s/ml0.pdf. Accessed July 3, 2022.
- A survey on machine learning approaches for automatic detection of voice disorders.J Voice. 2019; 33 (Available from:): 947.e11-947.e33
- Voice pathology detection and classification using auto- correlation and entropy features in different frequency regions.IEEE Access. 2018; 6: 6961-6974
- An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification.J Voice. 2017; : 31
- Sociodemographic characteristics and treatment response among aging adults with voice disorders in the United States.JAMA Otolaryngol Head Neck Surg. 2018; 144: 719-726
- Voice disorders and associated risk markers among young adults in the United States.Laryngoscope. 2017; 127: 2093-2099
- Patient-reported factors associated with the onset of hyperfunctional voice disorders.Ann Otology Rhinol Laryngol. 2021; 130: 389-394
- Direct speech feature estimation using an iterative EM algorithm for vocal fold pathology detection.IEEE Trans Biomed Eng. 1996; 43: 373-383
- Pathological voice quality assessment using artificial neural networks.Med Eng Phys. 2002; 24: 561-564
- Identification of voice disorders using speech samples.IEEE, 2003: 951-953
- K-means Nearest Neighbor Classifier for Voice Pathology.IEEE, Kharagpur, India2004: 352-354
- Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.IEEE Trans Biomed Eng. 2004; 51: 380-384
- Comparison of Neural Networks and Support Vector Machines Applied to Optimized Features Extracted From Patients’ Speech Signal for Classification of Vocal Fold Inflammation.IEEE, Athens, Greece2005: 844-8449
- Discrete Wavelet Transform and Support Vector Machine Applied to Pathological Voice Signals Identification.IEEE, Irvine, CA2005: 5
- Automatic diagnosis of pathological voices.WSEAS Trans Signal Process. 2006; 2: 1260-1267
- Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients’ speech signal with unilateral vocal fold paralysis.Comput Biol Med. 2007; 37: 474-485
- Telephony-based voice pathology assessment using automated speech analysis.IEEE Trans Biomed Eng. 2006; 53: 468-477
- Voice data mining for laryngeal pathology assessment.Comput Biol Med. 2016; 69: 270-276
- A comparison of multiple classification methods for diagnosis of Parkinson disease.Expert Syst Appl. 2010; 37: 1568-1572
Wroge T, Özkanca Y, Demiroglu C, et al. Parkinson's disease diagnosis using machine learning and voice. 2018. Available at: https://ieeexplore.ieee.org/abstract/document/8615607. Accessed February 8, 2022.
- An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease.Neurocomputing. 2016; 184: 131-144
- Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases.IEEE J Biomed Health Inf. 2015; 19: 1820-1828
- Pathological speech signal analysis and classification using empirical mode decomposition.Med Biol Eng Comput. 2013; 51: 811-821
- Objective assessment of pathological voice using artificial intelligence based on the GRBAS scale.J Voice. 2021; (Available at:) (Accessed April 17, 2022)
Fezari M, Amara F. Acoustic analysis for detection of voice disorders using adaptive features and classifiers. 2014. Available at: https://www.researchgate.net/profile/Mohamed-Fezari-2/publication/272093756_wwwinaseorg_library_2014_interlaken_bypaper_CSC_CSC-19/links/54db0ae00cf2ba88a68ee10a/wwwinaseorg-library-2014-interlaken-bypaper-CSC-CSC-19.pdf. Accessed May 18, 2022.
- Towards developing a voice pathologies detection system.J Commun Technol Electron. 2014; 59: 1280-1288
Chen L, Wang C, Chen J, et al. Voice disorder identification by using hilbert-huang transform (HHT) and K Nearest Neighbor (KNN). Elsevier. 2021. Available at: https://www.sciencedirect.com/science/article/pii/S0892199720301016?casa_token=4K5XDK2tDzEAAAAA:KpXPOyAXyhRkL5XxgqNICGmjhmJIU2nSxy39zv7bd2Qn_zOI04Ho1xyuJEgXRmqYKEY6k7DJ. Accessed May 18, 2022.
- Disease detection using analysis of voice parameters.Int J Comput Sci Commun Technol. 2012; 4: 6-10
Kadiri S, Alku P. Mel-frequency cepstral coefficients of voice source waveforms for classification of phonation types in speech. 2019. p. 2508–2512. https://www.apiit.edu.in/downloads/all%20chapters/CHAPTER-91.pdf
- On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices.Logopedics Phoniatrics Vocol. 2011; 36: 60-69
Kantardzic M. Data Reduction. New York, NY:John Wiley & Sons, Inc; 2003:53–86.
Chen L, Wang C, Chen J, et al. Voice disorder identification by using hilbert-huang transform (HHT) and K nearest neighbor (KNN). J Voice. 2020;35(6)
- Feature Analysis for Automatic Detection of Pathological Speech.IEEE, Houston, TX, USA2002: 182-183
- Feature Extraction Based on Mel-Scaled Wavelet Packet Transform for the Diagnosis of Voice Disorders.Springer, Berlin, Heidelberg2008: 790-793
- A joint time-frequency and matrix decomposition feature extraction methodology for pathological voice classification.EURASIP J Adv Signal Process. 2009; 2009
- Detection of vocal fold paralysis and edema using time-domain features and probabilistic neural network.Int J Biomed Eng Technol. 2011; 6: 46-57
- Voice pathology detection and discrimination based on modulation spectral features.IEEE Trans Audio Speech Lang Process. 2011; 19: 1938-1948
Tsanas A, Little MA, McSharry PE, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. 2012. Available at: https://ieeexplore.ieee.org/abstract/document/6126094/. Accessed May 19, 2022.
- An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine.Biomed Signal Process Control. 2012; 7: 3-19
- An efficient voice pathology classification scheme based on applying multi-layer linear discriminant analysis to wavelet packet-based features.Biomed Signal Process Control. 2014; 10: 209-223
- Detection of pathological voice using cepstrum vectors: a deep learning approach.J Voice. 2019; 33 (Available at:) (Accessed May 19, 2022): 634-641
- Differences and reliability of linear and nonlinear acoustic measures as a function of vocal intensity in individuals with voice disorders.J Voice. 2021; (8:S0892-1997(21)00144-2)
- What makes the cepstral peak prominence different to other acoustic correlates of vocal quality?.J Voice. 2020; 34: 806.e1-806.e6
- Voice feature selection to improve performance of machine learning models for voice production inversion.J Voice. 2021; (10:S0892-1997(21)00097-7)
- Analysis and classification of voice pathologies using glottal signal parameters.J Voice. 2016; 30: 549-556
- Endoscope motion compensation for laryngeal high-speed videoendoscopy.J Voice. 2005; 19: 485-496
- Characteristics of voice and personality of patients with vocal fold immobility.Codas. 2015; 27 (Available at:) (Accessed August 3, 2022): 178-185
- Recommended protocols for instrumental assessment of voice: american speech- language-hearing association expert panel to develop a protocol for instrumental assessment of vocal function.Angew Chem Int Ed. 1967; 6: 951-952
- Transfer function of Brazilian Portuguese oral vowels: a comparative acoustic analysis. 2009; 75: 680-684
- A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement.Comput Biol Med. 1990; 20: 337-340
Florencio V de O, Almeida A, Voice PBJ. Differences and reliability of linear and nonlinear acoustic measures as a function of vocal intensity in individuals with voice disorders. Elsevier. 2021. Available at: https://www.sciencedirect.com/science/article/pii/S0892199721001442?casa_token=bPaGKrqFaW4AAAAA:FRcA97bUvP-WKiV_QT8S4wGht6IJJFNQS15vmubgmMvlEiqakKkhUe13A_ug1NFw7M9Q3lyA. Accessed May 19, 2022.
- Exploiting nonlinearity of the speech production system for voice disorder assessment by recurrence quantification analysis.Chaos. 2018; 28: 085709-1-085709-10
- Effectiveness of recurrence quantification measures in discriminating subjects with and without voice disorders.J Voice. 2018; 34: 208-220
Chris Albon. Machine learning with python cookbook practical solutions from preprocessing to deep learning. 2018:304. https://www.docdroid.net/Z87gYoF/machine-learning-with-python-cookbook-en-pdf
- Machine learning. McGraw-Hill Science, New York1997: 432p
- Investigating the impact of data normalization on classification performance.Appl Soft Comput. 2020; 97105524
- Impact of data normalization on classification model accuracy.Research Papers Faculty of Materials Science and Technology Slovak University of Technology. 2019; 27: 79-84
- Investigating the impact of data normalization on classification performance.Appl Soft Comput. 2020; 97105524https://doi.org/10.1016/j.asoc.2019.105524
Kuhn M, Johnson K. Feature engineering and selection. feature engineering and selection: Boca Raton, Florida. 2020. http://www.feat.engineering/77
- A survey on machine learning approaches for automatic detection of voice disorders.J Voice. 2019; 33: 947.e11-947.e33https://doi.org/10.1016/j.jvoice.2018.07.014
- Predictive analytics and data mining.Business Intelligence. 2015; 15: 359-374
Li J, Cheng K, Wang S, et al. Feature selection: a data perspective. Vol. 50, ACM computing surveys. Association for Computing Machinery; 2017.
Steve Jadav. Voice-based gender identification using machine learning. https://ieeexplore.ieee.org/xpl/conhome/8766336/proceeding
- Applied Logistic Regression.The Statistician. 1991; 40: 458
- The measurement of observer agreement for categorical data.Biometrics. 1977; 33: 159
- Voice feature selection to improve performance of machine learning models for voice production inversion.J Voice. 2021; https://doi.org/10.1016/j.jvoice.2021.03.004
- On the measurement of discrimination against women.Am J Econ Sociol. 1979; 38: 287-292
- Voice disorder identification by using machine learning techniques.IEEE Access. 2018; 6: 16246-16255
- Performance of acoustic measures for the discrimination among healthy, rough, breathy, and strained voices using the feedforward neural network.J Voice. 2022; (Available at:) (Accessed October 27, 2022)
- Performance of different acoustic measures to discriminate individuals with and without voice disorders.J Voice. 2022; 36 (Available at:) (Accessed July 24, 2022)
- Cepstral measures in the assessment of severity of voice disorders.SciELO Brasil. 2019; (Available at:) (Accessed February 6, 2022)
Verde L, Pietro G de. Voice disorder identification by using machine learning techniques. 2018. Available at: https://ieeexplore.ieee.org/abstract/document/8316845/. Accessed February 8, 2022.
- Feature selection on magelang duck egg candling image using variance threshold method.in: 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020. 2020: 694-699
- A survey on machine learning approaches for automatic detection of voice disorders.J Voice. 2019; 33: 947.e11-947.e33
- Método de Aprendizagem de Máquina para Classificação da intensidade do desvio vocal utilizando Random Forest.J Health Inform. 2020; (Available at:) (Accessed July 9, 2021): 196-201
- Detection of pathological voice using cepstrum vectors: a deep learning approach.J Voice. 2019; 33: 634-641