Low-power keywords recognition has been a focus of acoustic signal processing for several
decades. This work investigates the domain-specific speech recognition microprocessor …

We describe an IC that provides a local speech recognition capability for a variety of
electronic devices. We start with a generic speech decoder architecture that is …

With the development of convolutional neural networks (CNN) across various domains, the
growth in network structure complexity and computational load has increasingly become a …

Speech recognition and keyword detection are becoming increasingly popular applications
for mobile systems. These applications have large memory and compute resource …

Exploiting the error tolerance in computation, approximate circuits become an emerging
computing paradigm to increase the energy efficiency in digital systems, which is crucial in …

As people become more comfortable with speaking to machines, the applications of speech
interfaces will diversify and include a wider range of devices, such as wearables …

Current trends towards on-edge computing on smart portable devices requires ultra-low
power circuits to be able to make feature extraction and classification tasks of patterns. This …

This paper gives an overview of acoustic modeling and search techniques for low-power
embedded ASR decoders. Our design decisions prioritize memory bandwidth, which is the …

Large Vocabulary Continuous Speech Recognition systems require Viterbi searching
through a large state space to find the most probable sequence of phonemes that led to a …

This paper describes a low-power VLSI chip for speaker-independent 60-kWord continuous
speech recognition based on a context-dependent Hidden Markov Model (HMM). We …