Glottal Biometric Features: Are Pathological Voice Studies appliable to Voice Biometry?

The purpose of the present paper is to introduce a methodology successfully used already in voice pathology detection for its possible adaptation to biometric speaker characterization as well. For such, the behavior of the same GMM classifiers used in the detection of pathology will be exploited. The work will show specific cases derived from running speech typically used in NIST contests against a Universal Background Model built from the population of normophonic subjects in specific vs general evaluation paradigms. Results are contrasted against a set of impostors derived from the same population of normophonic subjects. The relevance of the parameters used in the study will also be discusse

Glottal Parameter Estimation by Wavelet Transform for Voice Biometry

Voice biometry is classically based on the parameterization and patterning of speech features mainly. The present approach is based on the characterization of phonation features instead (glottal features). The intention is to reduce intra-speaker variability due to the `text'. Through the study of larynx biomechanics it may be seen that the glottal correlates constitute a family of 2-nd order gaussian wavelets. The methodology relies in the extraction of glottal correlates (the glottal source) which are parameterized using wavelet techniques. Classification and pattern matching was carried out using Gaussian Mixture Models. Data of speakers from a balanced database and NIST SRE HASR2 were used in verification experiments. Preliminary results are given and discussed

BioMet®Phon: A system to monitor phonation quality in the clinics

BioMet®Phon is a software application developed for the characterization of voice in voice quality evaluation. Initially it was conceived as plain research code to estimate the glottal source from voice and obtain the biomechanical parameters of the vocal folds from the spectral density of the estimate. This code grew to what is now the Glottex®Engine package (G®E). Further demands from users in laryngology and speech therapy fields instantiated the development of a specific Graphic User Interface (GUI’s) to encapsulate user interaction with the G®E. This gave place to BioMet®Phon, an application which extracts the glottal source from voice and offers a complete parameterization of this signal, including distortion, cepstral, spectral, biomechanical, time domain, contact and tremor parameters. The semantic capabilities of biomechanical parameters are discussed. Study cases from its application to the field of laryngology and speech therapy are given and discussed. Validation results in voice pathology detection are also presented. Applications to laryngology, speech therapy, and monitoring neurological deterioration in the elder are proposed

Relevance of the glottal pulse and the vocal tract in gender detection

Gender detection is a very important objective to improve efficiency in tasks as speech or speaker recognition, among others. Traditionally gender detection has been focused on fundamental frequency (f0) and cepstral features derived from voiced segments of speech. The methodology presented here consists in obtaining uncorrelated glottal and vocal tract components which are parameterized as mel-frequency coefficients. K-fold and cross-validation using QDA and GMM classifiers showed that better detection rates are reached when glottal source and vocal tract parameters are used in a gender-balanced database of running speech from 340 speakers

BioMet®Tools: from modeling and simulation to product design and development

BioMet®Tools is a set of software applications developed for the biometrical characterization of voice in different fields as voice quality evaluation in laryngology, speech therapy and rehabilitation, education of the singing voice, forensic voice analysis in court, emotional detection in voice, secure access to facilities and services, etc. Initially it was conceived as plain research code to estimate the glottal source from voice and obtain the biomechanical parameters of the vocal folds from the spectral density of the estimate. This code grew to what is now the Glottex®Engine package (G®E). Further demands from users in medical and forensic fields instantiated the development of different Graphic User Interfaces (GUI’s) to encapsulate user interaction with the G®E. This required the personalized design of different GUI’s handling the same G®E. In this way development costs and time could be saved. The development model is described in detail leading to commercial production and distribution. Study cases from its application to the field of laryngology and speech therapy are given and discussed

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

A methodology for monitoring emotional stress in phonation

Stress in phonation is mainly shown in the signature of the fundamental frequency. The proposed methodology is based on the estimation of the vocal fold biomechanics in terms of the distribution of the dynamic mass and the mechanical tension of the vocal fold structure. These parameters are derived from the reconstruction of the glottal source by inverse filtering. The vocal fold mechanical tension correlates (stress and strain), are used as the bases for tremor estimation. The correlates of tension and tremor are used to characterize the spontaneous speech of a database of 40 speakers of both genders (20 male and 20 female). Spontaneous speech consists in short interviews of 20 s of duration where the speakers have to express opinions on hot issues with which they are in agreement (pro) or in disagreement (con) following Arciuli's methodology. The emotional stress is estimated from the biomechanical correlates expressed above (tension and tremor). The null hypothesis formulated as the insensitivity of the speaker to pro and con situations has to be disregarded in view of the results for both genders. Interesting open questions are to be raised regarding the possibility of speakers consciously hiding their true opinion based on political correctness. The discussion will offer different hypotheses to further exploit the objective of detecting self-congruence in spoken messages