Keyword spotting is a classification task which aims to detect a specific set of spoken words. In general, this type of task runs on a power-constrained device such as a smartphone. One method to reduce the power consumption of a keyword spotting algorithm (typically a neural network) is to reduce the precision of the network weights and activations. In this paper, we propose a new representation of speech features which is more adapted to low-precision networks and compatible with binary/ternary neural networks. The new representation is based on the log-Mel spectrogram and models the variation of power over time. Tested on a ResNet, this representation produces results nearly as accurate as full-precision MFCCs, which are traditionally used in speech recognition applications.

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