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We have developed a low-power VLSI chip for 60-kWord real-time continuous speech recognition based on a context-dependent Hidden Markov Model (HMM). Our implementation includes a cache architecture using locality of speech recognition, beam pruning using a dynamic threshold, two-stage language model searching, highly parallel Gaussian Mixture Model (GMM) computation based on the mixture level, a variable-frame look-ahead scheme, and elastic pipeline operation between the Viterbi transition and GMM processing. Results show that our implementation achieves 95% bandwidth reduction (70.86 MB/s) and 78% required frequency reduction (126.5 MHz). The test chip, fabricated using 40 nm CMOS technology, contains 1.9 M transistors for logic and 7.8 Mbit on-chip memory. It dissipates 144 mW at 126.5 MHz and 1.1 V for 60 kWord real-time continuous speech recognition. [ABSTRACT FROM PUBLISHER]