Victor Nieto Lluis

Glottal Source biometrical signature for voice pathology detection

The Glottal Source is an important component of voice as it can be considered as the excitation signal to the voice apparatus. The use of the Glottal Source for pathology detection or the biometric characterization of the speaker are important objectives in the acoustic study of the voice nowadays. Through the present work a biometric signature based on the speakers power spectral density of the Glottal Source is presented. It may be shown that this spectral density is related to the vocal fold cover biomechanics, and from literature it is well-known that certain speakers features as gender, age or pathologic condition leave changes in it. The paper describes the methodology to estimate the biometric signature from the power spectral density of the mucosal wave correlate, which after normalization can be used in pathology detection experiments. Linear Discriminant Analysis is used to confront the detection capability of the parameters defined on this glottal signature among themselves and compared to classical perturbation parameters. A database of 100 normal and 100 pathologic subjects equally balanced in gender and age is used to derive the best parameter cocktails for pathology detection and quantification purposes to validate this methodology in voice evaluation tests. In a study case presented to illustrate the detection capability of the methodology exposed a control subset of 24+24 subjects is used to determine a subjects voice condition in a pre- and post-surgical evaluation. Possible applications of the study can be found in pathology detection and grading and in rehabilitation assessment after treatment.

DOA Detection from HOS by FOD Beamforming and Joint-Process Estimation.

Array Beamforming is a powerful technique in Speech Enhancement, Noise Reduction, Source Separation, etc. for which powerful techniques have been developed [8]. Nevertheless, large Arrays present several inconveniences, as are sensor equalization, complex DOA algorithms, high costs, large computational requirements, etc. This lead to exploring other possible structures based on paired sensors, as First-Order Differential Beamformers (FODB) [2]. These structures may be steered to aim their sharp notch to the desired source, which may be removed from the output, and complementarily reconstructed using several methods, as direct or spectral subtraction [1], or joint-process estimation [5]. The main problem that these systems present is DOA estimation in the presence of reverberation. Through this paper it is shown that the use of Higher Order Statistics may help in detecting DOA’s. Results for simulated source separation, and DOA detection in a real room are given and discussed.