### ASA 124th Meeting New Orleans 1992 October

## 5pSP2. Speaker-independent connected digit recognition algorithm for a
single chip signal processor.

**Stephen C. Glinski
**

**
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*AT&T Bell Labs., 600 Mountain Ave., Rm. 2A-402, Murray Hill, NJ 07974
*

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An algorithm was designed for a fixed point arithmetic signal processor
chip to perform real-time speaker-independent English digit recognition. Each
word was represented by a single 10-state Markov model, the states of which
were 9-way mixtures of 24-dimensional Gaussian densities of cepstral features.
Algorithms for feature extraction include autocorrelation, linear predictive
coding, and the computation of both cepstra and differential cepstra.
Algorithms for pattern matching include a Laplacian distance measure, viterbi
decoder, best choice, and partial traceback. To achieve real-time operation,
single precision arithmetic was employed for the Laplacian distance metric,
which is the bottleneck in the recognizer. Memory storage was minimized by
quantizing model parameters to 10 bits and dynamically pruning a tree of word
candidates. Recognition accuracy of about 98% per word was obtained; this is
approximately the same as that obtained with a floating point simulator as
tested on a connected digits NIST database.