DISCUSSION: applications for real-time clustering chips

[Bernabe Linares B.]

This message is in hopes of starting a discussion about the 
usefulness of real-time clustering chips.

In our institution we are developing a real-time clustering
chip for possible application in speech recognition, where 
as the speaker changes, a certain adaptation needs to be
performed. I have identified in the literature several ways
of doing this (see refs. [1]-[7] below).

Our chip is able to cluster 100-binary-pixels input patterns
into up to 18 different categories. By assembling an NxM
array of these chips Nx100-binary-pixels patterns can be
clustered into up to Mx18 categories. Patterns are classified
(and the corresponding clusters are re-adapted) in less than
1 micro-second.

The discussion I would like to start is wether or not these
kind of chips are useful for this or other applications. If
you have experience with any application in which it would
be desirable to have a real-time clustering chip, please
enter the discussion. If possible, indicate the type of
patterns that would need to be real-time-clustered (binary,
digital, or analog), speed at which this clustering would be
desirable, and other requirements you would desire. Please
describe briefly your application and provide some typical
references (if possible).

I am not aware of other chips of this nature that are
reported in the literature. For a brief description of our
chip please see ref. [8] (a copy is available in the
neuroprose archieve as pub/neuroprose/bernabe.art1chip.ps.Z).

Dr. Bernabe Linares-Barranco
National Microelectronics Center (CNM)
Ed. CICA, Av. Reina Mercedes s/n
41012 Sevilla, SPAIN
Phone: 34-5-4239923
FAX:   34-5-4624506
E-mail: bernabe at cnm.us.es

References:

[1]  J. B. Hampshire and A. H. Waible, "The Meta-Pi network:
     connectionist rapid adaptation for high-performance
     multi-speaker phoneme recognition," ICASSP'90, pp. 165-
     168, vol. 1, 1990.

[2]  Y. Gang and J. P. Haton, "Signal-to-string conversion
     based on high likelihood regions using embedded dynamic
     processing," IEEE Trans. on Pattern Analysis and Machine
     Intelligence, vol. 13, No. 3, pp. 297-302, March 1991.

[3]  G. Rigoll, "Baseform adaptation for large vocabulary
     hidden markov model based speech recognition systems",
     ICASSP'90, pp. 141-144, vol. 1.

[4]  S. Cox, "Speaker adaptation in speech recognition using
     linear regression techniques," Electronics Letters,
     vol. 28, No. 22, pp. 2093-2094, 22 Oct. 1992.

[5]  P. G. Bamberg and M. A. Mandel, "Adaptable phoneme-based
     models for large-vocabulary speech recognition," Speech
     Communication, vol. 10, No. 5-6, pp. 437-451, Dec. 1991.

[6]  X. Huang and K. F. Lee, "On speaker-independent,
     speaker-dependent, and speaker-adaptive speech recognition,"
     IEEE Trans. on Speech and Audio Processing, vol. 1, No. 2,
     pp. 150-157, April 1993.

[7]  M. Witbrock and P. Haffner, "Rapid connectionist speaker
     adaptation," ICASSP'92, pp. 453-456, vol. 1.

[8]  T. Serrano, B. Linares-Barranco, and J. L. Huertas, "A CMOS
     VLSI Analog Current-Mode High-Speed ART1 Chip," Proc. of the
     1994 IEEE Int. Conference on Neural Networks, Orlando,
     Florida, July 1994, pp. 1912-1916, vol. 3.