Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010

Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is 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

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

Bio-inspired Dynamic Formant Tracking for Phonetic Labelling

It is a known fact that phonetic labeling may be relevant in helping current Automatic Speech Recognition (ASR) when combined with classical parsing systems as HMM's by reducing the search space. Through the present paper a method for Phonetic Broad-Class Labeling (PCL) based on speech perception in the high auditory centers is described. The methodology is based in the operation of CF (Characteristic Frequency) and FM (Frequency Modulation) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus in the automatic detection of formants and formant dynamics on speech. Results obtained informant detection and dynamic formant tracking are given and the applicability of the method to Speech Processing is discussed

A Hybrid Parameterization Technique for Speaker Identification

Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. 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. The separation methodology is based in Joint Process Estimation under the un-correlation hypothesis between vocal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency domains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speakers are shown comparing different parameterization strategies. The results confirm the better performance of decoupled parameterization compared against approaches based on plain speech parameterization

Bioaccessible peptides released by in vitro gastrointestinal digestion of fermented goat milks

In this study, ultrafiltered goat milks fermented with the classical starter bacteria Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus salivarus subsp. thermophilus or with the classical starter plus the Lactobacillus plantarum C4 probiotic strain were analyzed using ultra-high performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS) and/or liquid chromatography-ion trap (LC-IT-MS/MS). Partial overlapping of the identified sequences with regard to fermentation culture was observed. Evaluation of the cleavage specificity suggested a lower proteolytic activity of the probiotic strain. Some of the potentially identified peptides had been previously reported as angiotensin converting enzyme (ACE)-inhibitory, antioxidant and antibacterial and might account for the in vitro activity previously reported for these fermented milks. Simulated digestion of the products was conducted in presence of a dialysis membrane to retrieve the bioaccessible peptide fraction. Some sequences with reported physiological activity resisted digestion but were found in the non-dialyzable fraction. However, non-previously detected sequences such as the antioxidant αs1-casein 144YFYPQL149, the antihypertensive αs2-casein 90YQKFPQY96 and the antibacterial αs2-casein 165LKKISQ170 were found in the dialyzable fraction of both fermented milks. Moreover, in the fermented milk including the probiotic strain, the k-casein dipeptidyl peptidase IV inhibitor (DPP-IV) 51INNQFLPYPY60 as well as additional ACE-inhibitory or antioxidant sequences could be identified. With the aim to anticipate further biological outcomes, quantitative structure activity relationship (QSAR) analysis was applied to the bioaccessible fragments and led to propose potential ACE inhibitory sequences

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

Antioxidant, ACE-inhibitory and antimicrobial activity of fermented goat milk: activity and physicochemical property relationship of the peptide components

Increasing evidence on goat milk and the health benefits of its derived products beyond its nutritional value show its potential as a functional food. In this study, goat milk fractions were tested for their total antioxidant capacity using different methods (ORAC, ABTS, DPPH and FRAP), as well as their angiotensin-I-converting-enzyme inhibitory and antimicrobial (against Escherichia coli and Micrococcus luteus) activities. Different whey fractions (whey, cation exchange membrane permeate P and retentate R) of two fermented skimmed goat milks (ultrafiltered goat milk fermented with the classical starter bacteria or with the classical starter plus the Lactobacillus plantarum C4 probiotic strain) were assessed. Additionally, P fractions were divided into two sub-fractions after being passed through a 3 kDa cut-off membrane: (a) the permeate with peptides of MW 3 kDa (P > 3). No differences in biological activities were observed between the two fermented milks. However, the biological peptides present in the P < 3 fraction showed the highest total antioxidant capacity (for the ORAC assay) and angiotensin-I-converting-enzyme inhibitory activity. Those present in the R fraction showed the highest total antioxidant capacity against ABTS˙+ and DPPH˙ radicals. Some antimicrobial activity against E. coli was observed for the fermented milk containing the probiotic, which could be due to some peptides being released by the probiotic strain. In conclusion, small and non-basic bioactive peptides could be responsible for most of the angiotensin-I-converting-enzyme inhibitory and antioxidant activities. These findings reinforce the potential benefits of the consumption of fermented goat milk in the prevention of cardiovascular diseases associated with oxidative stress and hypertension

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