Campus-based publishing partnerships: A guide to critical issues

Campus-based publishing partnerships offer the academy greater control over the intellectual products that it helps create. To fully realize this potential, such partnerships will need to evolve from informal working alliances to long-term, programmatic collaborations. SPARCâ s Campus-based Publishing Partnerships: A Guide to Critical Issues addresses issues relevant to building sound and balanced partnerships, including: Establishing governance and administrative structures; Identifying funding models that accommodate the objectives of both libraries and presses; Defining a partnershipâ s objectives to align the missions of the library and the press; Determining what services to provide; and Demonstrating the value of the collaboration. SPARCâ s Campus-based Publishing Partnerships will help libraries, presses, and academic units to define effective partnerships capable of supporting innovative approaches to campus-based publishing.

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent 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.