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This paper describes a low-power VLSI chip for speakerindependent 60-kWord continuous speech recognition based on a contextdependent Hidden Markov Model (HMM). It features a compressiondecoding scheme to reduce the external memory bandwidth for Gaussian Mixture Model (GMM) computation and multi-path Viterbi transition units. We optimize the internal SRAM size using the max-approximation GMM calculation and adjusting the number of look-ahead frames. The test chip, fabricated in 40 nm CMOS technology, occupies 1.77 mm × 2.18 mm containing 2.52 M transistors for logic and 4.29 Mbit on-chip memory. The measured results show that our implementation achieves 34.2% required frequency reduction (83.3 MHz), 48.5% power consumption reduction (74.14 mW) for 60 k-Word real-time continuous speech recognition compared to the previous work while 30% of the area is saved with recognition accuracy of 90.9%. This chip can maximally process 2.4× faster than real-time at 200 MHz and 1.1 V with power consumption of 168 mW. By increasing the beam width, better recognition accuracy (91.45%) can be achieved. In that case, the power consumption for real-time processing is increased to 97.4 mW and the max-performance is decreased to 2.08× because of the increased computation workload. key words: 40 nm VLSI, hidden Markov model (HMM), large vocabulary continuous recognition (LVCSR)

A 168-mW 2.4×-Real-Time 60-kWord Continuous Speech Recognition Processor VLSI