Glottal-Source Spectral Biometry for Voice Characterization

The biometric signature derived from the estimation of the power spectral density singularities of a speaker’s glottal source is described in the present work. This consists in the collection of peak-trough profiles found in the spectral density, as related to the biomechanics of the vocal folds. Samples of parameter estimations from a set of 100 normophonic (pathology-free) speakers are produced. Mapping the set of speaker’s samples to a manifold defined by Principal Component Analysis and clustering them by k-means in terms of the most relevant principal components shows the separation of speakers by gender. This means that the proposed signature conveys relevant speaker’s metainformation, which may be useful in security and forensic applications for which contextual side information is considered relevant

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

Using dysphonic voice to characterize speaker's biometry

Phonation distortion leaves relevant marks in a speaker's biometric profile. Dysphonic voice production may be used for biometrical speaker characterization. In the present paper phonation features derived from the glottal source (GS) parameterization, after vocal tract inversion, is proposed for dysphonic voice characterization in Speaker Verification tasks. The glottal source derived parameters are matched in a forensic evaluation framework defining a distance-based metric specification. The phonation segments used in the study are derived from fillers, long vowels, and other phonation segments produced in spontaneous telephone conversations. Phonated segments from a telephonic database of 100 male Spanish native speakers are combined in a 10-fold cross-validation task to produce the set of quality measurements outlined in the paper. Shimmer, mucosal wave correlate, vocal fold cover biomechanical parameter unbalance and a subset of the GS cepstral profile produce accuracy rates as high as 99.57 for a wide threshold interval (62.08-75.04%). An Equal Error Rate of 0.64 % can be granted. The proposed metric framework is shown to behave more fairly than classical likelihood ratios in supporting the hypothesis of the defense vs that of the prosecution, thus ofering a more reliable evaluation scoring. Possible applications are Speaker Verification and Dysphonic Voice Grading

Decoupling Vocal Tract from Glottal Source Estimates in Speaker's Identification

Classical parameterization techniques in Speaker Identification tasks use the codification of the power spectral density of speech as a whole, not discriminating between articulatory features due to the dynamics of vocal tract (acoustic-phonetics) and those contributed by the glottal source. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. In this way information which is conveyed in both components depending in different degree on message and biometry is estimated and treated differently to be fused at the time of template composition. The methodology to separate both components is based on the decorrelation hypothesis between vocal and glottal information and it is carried out using Joint Process Estimation. This methodology is briefly discussed and its application on vowel-like speech is presented as an example to observe the resulting estimates both in the time as in the frequency domain. The parameterization methodology to produce representative templates of the glottal and vocal components is also described. Speaker Identification experiments conducted on a wide database of 240 speakers is also given with comparative scorings obtained using different parameterization strategies. The results confirm the better performance of de-coupled parameterization techniques compared against approaches based on full speech parameterization

Monitoring Neurological disease in Phonation

It is well known that many neurological diseases leave a fingerprint in voice and speech production. The dramatic impact of these pathologies in life quality is a growing concert. Many techniques have been designed for the detection, diagnose and monitoring the neurological disease. Most of them are costly or difficult to extend to primary services. The present paper shows that some neurological diseases can be traced a the level of voice production. The detection procedure would be based on a simple voice test. The availability of advanced tools and methodologies to monitor the organic pathology of voice would facilitate the implantation of these tests. The paper hypothesizes some of the underlying mechanisms affecting the production of voice and presents a general description of the methodological foundations for the voice analysis system which can estimate correlates to the neurological disease. A case of study is presented from spasmodic dysphonia to illustrate the possibilities of the methodology to monitor other neurological problems as well

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

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

Neurological Disease Detection and Monitoring from Voice Production

The dramatic impact of neurological degenerative pathologies in life quality is a growing concern. It is well known that many neurological diseases leave a fingerprint in voice and speech production. Many techniques have been designed for the detection, diagnose and monitoring the neurological disease. Most of them are costly or difficult to extend to primary attention medical services. Through the present paper it will be shown how some neurological diseases can be traced at the level of phonation. The detection procedure would be based on a simple voice test. The availability of advanced tools and methodologies to monitor the organic pathology of voice would facilitate the implantation of these tests. The paper hypothesizes that some of the underlying mechanisms affecting the production of voice produce measurable correlates in vocal fold biomechanics. A general description of the methodological foundations for the voice analysis system which can estimate correlates to the neurological disease is shown. Some study cases will be presented to illustrate the possibilities of the methodology to monitor neurological diseases by voic