addition of new feature components to the frame vectors.We investigate a class of features related to voicing parameters that indicate whether the vocal chords are vibrating. Features describing voicing characteristics of speech signals are integrated with an existing 38-dimensional feature vector consisting of first and second order time derivatives of the frame energy and of the cepstral coefficients with their first and second derivatives. HMM-based connected digit recognition experiments comparing the traditional and extended feature sets show that voicing features and spectral information are complementary and that improved speech recognition performance is obtained by combining the two sources of information. 21 0-7803-4428-6/98 $10.00 0 I998 IEEE

In order to pilot a shift towards greater use of collaborative learning in our higher education programs, the University of Hong Kong has invested in the development of a prototype technology-enhanced collaborative learning space. The space was created by retrofitting a vacant studio, turning it into an innovative classroom space in which collaborative learning is promoted and facilitated both through the provision of technology and by the physical layout of the room. We have used the space to pioneer collaborative learning both by holding professional development workshops for faculty in the room and also by helping academic staff to run experimental courses in the learning space. The opportunity to offer professional development and support for academic staff in this environment is particularly valuable as it ensures they do not simply deliver traditional didactic lectures in a space designed to promote interactive student learning and engagement. By using the space as a 'student' they are able to consider how they may use collaborative learning environments with their students. This paper describes use of the room for professional development of academic staff and also provides two examples of the use and evaluation of the room by faculty who used the room to teach experimental classes.

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Advances in automatic speech recognition (ASR) technology promise new products and services that capitalize on the enhanced capability of human‐machine communication. This paper: Addresses several key issues in the design of a deployable ASR, Discusses new dimensions and developments of the technology, Presents several examples of successful applications of the ASR technology in AT&T's products and services, and Describes the research effort needed to realize many new telecommunications applications in years to come.

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