By Jorge Baptista
This publication constitutes the refereed court cases of the eleventh foreign Workshop on Computational Processing of the Portuguese Language, PROPOR 2014, held in Sao Carlos, Brazil, in October 2014. The 14 complete papers and 19 brief papers provided during this quantity have been conscientiously reviewed and chosen from sixty three submissions. The papers are prepared in topical sections named: speech language processing and purposes; linguistic description, syntax and parsing; ontologies, semantics and lexicography; corpora and language assets and ordinary language processing, instruments and applications.
Read or Download Computational Processing of the Portuguese Language: 11th International Conference, PROPOR 2014, São Carlos/SP, Brazil, October 6-8, 2014. Proceedings PDF
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Extra resources for Computational Processing of the Portuguese Language: 11th International Conference, PROPOR 2014, São Carlos/SP, Brazil, October 6-8, 2014. Proceedings
Building a GMM Supervector. A GMM supervector is constructed by stacking the means of the adapted mixture components. 2 "Smile" Features The “Smile” set, derived from the openSMILE toolkit , corresponds to 68 measures (“functionals”) applied to a vector of 39 MFCC parameters (statics and dynamics MFCC coefficients). The “functionals” can be divided into six groups: Extremes, Moments, Percentiles, Times, Means and Peaks. Extremes contains 5 parameters (maximum, minimum, range, mean frames between maximum values and mean of frames between minimum values); Moments contains 4 parameters (variance, Characterizing Parkinson's Disease Speech by Acoustic and Phonetic Features 31 skewness, kurtosis and standard deviation); Percentiles contains 9 parameters (3 quartiles, 3 inter-quartile ranges, percentiles 1 and 99 and inter-percentile ranges), Times contains 10 parameters (4 up and down-level times, raise and fall times), Means contains 11 parameters (including arithmetic mean, arithmetic mean of absolute values, quadratic mean among others) and Peaks contains 29 parameters (including number of peaks, mean of frames between peaks, standard deviation of frames between peaks among others).
The performance is undoubtedly boosted by the complete lack of test data from speakers aged 11- 24. 4 ASR Experiments This section describes the ASR experiments carried out to test the potential benefits of automatic age group classification in ASR. We used three different sets of Hidden Markov Model (HMM) models for the experiments: “standard” AMs trained using young to middle-aged adults’ speech (hereafter “SAM”), as well as two separate sets of AMs specifically optimised for children’s and elderly people’s speech (hereafter “CAM” and “EAM”), respectively.
For instance, for Brazilian Portuguese (BP) - standard pronunciation of São Paulo should the verbal form
Computational Processing of the Portuguese Language: 11th International Conference, PROPOR 2014, São Carlos/SP, Brazil, October 6-8, 2014. Proceedings by Jorge Baptista