Building global products and competing in innovation: The role of chinese university spin-outs and required innovation capabilities

Usually, firms that produce innovative global products are discussed within the context of developed countries. New ventures in developing countries are typically viewed as low-cost product providers that generate technologically similar products to those produced by developed economies. However, this paper argues that some Chinese university spin-outs (USOs), although rare, have adopted a novel 'catch-up' strategy to build global products on the basis of indigenous platform technologies. This paper attempts to develop a conceptual framework to address the question: how do these specific Chinese USOs develop their innovation capabilities to build global products? In order to explore the idiosyncrasies of the specific USOs, this paper uses the multiple case studies method. The primary data sources are accessed through semi-structured interviews. In addition, archival data and other materials are used as secondary sources. The study analyses the configuration of capabilities that are needed for idiosyncratic growth, and maps them to the globalisation processes. This paper provides a strategic 'roadmap' as an explanatory guide to entrepreneurs, policy makers and investors to better understand the phenomena. © 2014 Inderscience Enterprises Ltd.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. In normal cross adaptation it is assumed that useful diversity among systems exists only at acoustic level. However, complimentary features among complex LVCSR systems also manifest themselves in other layers of modelling hierarchy, e.g., subword and word level. It is thus interesting to also cross adapt language models (LM) to capture them. In this paper cross adaptation of multi-level LMs modelling both syllable and word sequences was investigated to improve LVCSR system combination. Significant error rate gains up to 6.7% rel. were obtained over ROVER and acoustic model only cross adaptation when combining 13 Chinese LVCSR subsystems used in the 2010 DARPA GALE evaluation. © 2010 ISCA.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.