Automatic detection of voice impairments from text-dependent running speech

Acoustic analysis is a useful tool to diagnose voice diseases. Furthermore it presents several advantages: it is non-invasive, provides 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 voiced frame with mel-frequency cepstral parameters. The classification is carried out using a discriminative approach based on a multilayer perceptron neural 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 140 patients (23 normal and 117 pathological). The results obtained are compared with those using sustained vowels. The text-dependent running speech showed a light improvement in the accuracy of the detection.