Manejo dietético-nutricional de alergias e intolerancias alimentarias en las dietas plant-based

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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

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

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®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

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®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

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