Fifth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2007)
Most of the vocal and voice diseases cause
changes in the acoustic voice signal. Acoustic analysis
is a useful tool to diagnose this kind of diseases,
furthermore it presents several advantages: it is a
non-invasive tool, an objective diagnostic and, also, it
can be used for the evaluation of surgical and
pharmacological treatments and rehabilitation
processes. Most of the approaches found in the
literature address the automatic detection of voice
impairments from speech by using the sustained
phonation of vowels. In this paper it is proposed a new
scheme for the detection of voice impairments from
text dependent running speech. The proposed
methodology is based on the segmentation of speech
into voiced and non voiced frames, parameterising
each frame with mel frequency cepstral parameters.
The classification is carried out using a discriminative
approach based on a Multilayer Perceptron Network.
The data used to train the system were taken from the
voice disorders database distributed by Kay
Elemetrics. The material used for training and testing
contains the running speech corresponding to the well
known rainbow passage of 226 patients (53 normal
and 173 pathological). The results obtained are
compared with those using sustained vowels. The textdependent
running speech showed a light
improvement in the accuracy of the detection.
Index Terms. running speech, pathological voices, mel cepstral parameters, multilayer perceptron
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Godino-Llorente, Juan Ignacio / Fraile, Rubén / Sáenz-Lechón, Nicolás / Osma-Ruiz, Víctor / Gómez-Vilda, Pedro (2007): "Automatic detection of voice impairments from text-dependent running speech using a discriminative approach", In MAVEBA-2007, 25-28.