title: Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.
abstract: It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.
authors.affiliation: Universidad Politécnica de Madrid, Escuela Universitaria de Ingeniería Técnica de Telecomunicación, Dpt. of Ingeniería de Circuitos y Sistemas, Ctra. Valencia Km. 7, 28031, Madrid. igodino@ics.upm.es
authors.forename: J I
authors.initials: JI
authors.lastname: Godino-Llorente
authors.forename: P
authors.initials: P
authors.lastname: Gómez-Vilda
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D009415:Nerve Net; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D014832:Voice Disorders / Q000145:classification* / Q000175:diagnosis*; D014833:Voice Quality