Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Measuring mean densities of delta Scuti stars with asteroseismology. Theoretical properties of large separations using TOUCAN

We aim at studying the theoretical properties of the regular spacings found in the oscillation spectra of delta Scuti stars. We performed a multi-variable analysis covering a wide range of stellar structure and seismic properties and model parameters representative of intermediate-mass, main sequence stars. The work-flow is entirely done using a new Virtual Observatory tool: TOUCAN (the VO gateway for asteroseismic models), which is presented in this paper. A linear relation between the large separation and the mean density is predicted to be found in the low frequency frequency domain (i.e. radial orders spanning from 1 to 8, approximately) of the main-sequence, delta Scuti stars' oscillation spectrum. We found that such a linear behavior stands whatever the mass, metallicity, mixing length, and overshooting parameters considered in this work. The intrinsic error of the method is discussed. This includes the uncertainty in the large separation determination and the role of rotation. The validity of the relation found is only guaranteed for stars rotating up to 40 percent of their break-up velocity. Finally, we applied the diagnostic method presented in this work to five stars for which regular patterns have been found. Our estimates for the mean density and the frequency of the fundamental radial mode match with those given in the literature within a 20 percent of deviation. Asteroseismology has thus revealed an independent direct measure of the average density of delta Scuti stars, analogous to that of the Sun. This places tight constraints on the mode identification and hence on the stellar internal structure and dynamics, and allows a determination the radius of planets orbiting around delta Scuti stars with unprecedented precision. This opens the way for studying the evolution of regular patterns in pulsating stars, and its relation with stellar structure and evolution.Comment: 11 pages, 6 figures, A&A in pres