7 resultados para glottal closure

em Universidad Politécnica de Madrid


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The Glottal Source correlates reconstructed from the phonated parts of voice may render interesting information with applicability in different fields. One of them is defective closure (gap) detection. Through the paper the background to explain the physical foundations of defective gap are reviewed. A possible method to estimate defective gap is also presented based on a Wavelet Description of the Glottal Source. The method is validated using results from the analysis of a gender-balanced speakers database. Normative values for the different parameters estimated are given. A set of study cases with deficient glottal closure is presented and discussed.

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A case study of vocal fold paralysis treatment is described with the help of the voice quality analysis application BioMet®Phon. The case corresponds to a description of a 40 - year old female patient who was diagnosed of vocal fold paralysis following a cardio - pulmonar intervention which required intubation for 8 days and posterior tracheotomy for 15 days. The patient presented breathy and asthenic phon ation, and dysphagia. Six main examinations were conducted during a full year period that the treatment lasted consisting in periodic reviews including video - endostroboscopy, voice analysis and breathing function monitoring. The phoniatrician treatment inc luded 20 sessions of vocal rehabilitation, followed by an intracordal infiltration with Radiesse 8 months after the rehabilitation treatment started followed by 6 sessions of rehabilitation more. The videondoscopy and the voicing quality analysis refer a s ubstantial improvement in the vocal function with recovery in all the measures estimated (jitter, shimmer, mucosal wave contents, glottal closure, harmonic contents and biomechanical function analysis). The paper refers the procedure followed and the results obtained by comparing the longitudinal progression of the treatment, illustrating the utility of voice quality analysis tools in speech therapy.

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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.

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In order to obtain more human like sounding humanmachine interfaces we must first be able to give them expressive capabilities in the way of emotional and stylistic features so as to closely adequate them to the intended task. If we want to replicate those features it is not enough to merely replicate the prosodic information of fundamental frequency and speaking rhythm. The proposed additional layer is the modification of the glottal model, for which we make use of the GlottHMM parameters. This paper analyzes the viability of such an approach by verifying that the expressive nuances are captured by the aforementioned features, obtaining 95% recognition rates on styled speaking and 82% on emotional speech. Then we evaluate the effect of speaker bias and recording environment on the source modeling in order to quantify possible problems when analyzing multi-speaker databases. Finally we propose a speaking styles separation for Spanish based on prosodic features and check its perceptual significance.

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This paper presents a description of our system for the Albayzin 2012 LRE competition. One of the main characteristics of this evaluation was the reduced number of available files for training the system, especially for the empty condition where no training data set was provided but only a development set. In addition, the whole database was created from online videos and around one third of the training data was labeled as noisy files. Our primary system was the fusion of three different i-vector based systems: one acoustic system based on MFCCs, a phonotactic system using trigrams of phone-posteriorgram counts, and another acoustic system based on RPLPs that improved robustness against noise. A contrastive system that included new features based on the glottal source was also presented. Official and postevaluation results for all the conditions using the proposed metrics for the evaluation and the Cavg metric are presented in the paper.

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The Mt. Amiata volcano (Tuscany, central Italy) hosts the second largest geothermal field of Italy. Its SW and NE sectors are characterized by the presence of several CO2-rich (mayor que95% by vol.) gas discharges. An intense Hg mining activity had taken place from the 19th century up to the end of the ?70s, particularly close to Abbadia San Salvatore, during which two drillings (Acqua Passante and Ermeta) intercepted a CO2-rich gas fertile horizon. The related gases are emitted in the atmosphere since 1938 and 1959, respectively, causing severe concerns for the local air quality. In this work the results of a geochemical and isotopic survey carried out on these gas emissions from March 2009 to January 2014 are presented. CO2 fluxes from both the two wells and soil from an area of about 653,500 m2 located between them were measured. The two wells are emitting up to 15,000, 92 and 8 tons y-1 of CO2, CH4 and H2S, respectively, while the computed soil CO2 output was estimated at 4,311 ton y-1. The spatial distribution of the CO2 soil flux suggests the presence of preferential patterns, indicating sites of higher permeability. Since the local municipality is evaluating the possibility to plug the Ermeta vent, a temporarily closure should first be carried out to test the possible influence of this operation on the diffuse soil degassing of deep-originated CO2 in the surrounding area. This implies that diffuse soil gases should carefully be monitored before proceeding with its definitive closure.

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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.