Praat : doing phonetics by computer
01 Jan 2006-
About: The article was published on 2006-01-01 and is currently open access. It has received 5265 citations till now. The article focuses on the topics: Phonetics.
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25 Oct 2010
TL;DR: The openSMILE feature extraction toolkit is introduced, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities and has a modular, component based architecture which makes extensions via plug-ins easy.
Abstract: We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.
2,286 citations
TL;DR: This paper starts with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling and elaborate advanced computational techniques to address robustness and session variability.
1,433 citations
TL;DR: This study systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI data collected while subjects listened to hours of narrative stories, and uses a novel generative model to create a detailed semantic atlas.
Abstract: The meaning of language is represented in regions of the cerebral cortex collectively known as the 'semantic system'. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is unknown. Here we systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI (fMRI) data collected while subjects listened to hours of narrative stories. We show that the semantic system is organized into intricate patterns that seem to be consistent across individuals. We then use a novel generative model to create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent information about specific semantic domains, or groups of related concepts, and our atlas shows which domains are represented in each area. This study demonstrates that data-driven methods--commonplace in studies of human neuroanatomy and functional connectivity--provide a powerful and efficient means for mapping functional representations in the brain.
1,120 citations
TL;DR: The effects of music training in relation to brain plasticity have caused excitement, evident from the popularity of books on this topic among scientists and the general public as discussed by the authors, which suggests that, akin to physical exercise and its impact on body fitness, music is a resource that tones the brain for auditory fitness.
Abstract: The effects of music training in relation to brain plasticity have caused excitement, evident from the popularity of books on this topic among scientists and the general public. Neuroscience research has shown that music training leads to changes throughout the auditory system that prime musicians for listening challenges beyond music processing. This effect of music training suggests that, akin to physical exercise and its impact on body fitness, music is a resource that tones the brain for auditory fitness. Therefore, the role of music in shaping individual development deserves consideration.
855 citations
TL;DR: A new measure of dysphonia, pitch period entropy (PPE), is introduced, which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency, and is well suited to telemonitoring applications.
Abstract: In this paper, we present an assessment of the practical value of existing traditional and nonstandard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, pitch period entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected ten highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that nonstandard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected nonstandard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well suited to telemonitoring applications.
816 citations