Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature

Abstract

This paper proposes a small-footprint wake-up-word (WUW) recognition system for real noisy environments by employing the competing-words-based feature. Competing-words-based features are generated using a ResNet-based deep neural network with small parameters using the competing-words dataset. The competing-words dataset consists of the most acoustically similar and dissimilar words to the WUW used for our system. The obtained features are used as input to the classification network, which is developed using the convolutional neural network (CNN) model. To obtain sufficient data for training, data augmentation is performed by using a room impulse response filter and adding sound signals of various television shows as background noise, which simulates an actual living room environment. The experimental results demonstrate that the proposed WUW recognition system outperforms the baselines that employ CNN and ResNet models. The proposed system shows 1.31% in equal error rate and 1.40% false rejection rate at a 1.0% false alarm rate, which are 29.57% and 50.00% relative improvements compared to the ResNet system, respectively. The number of parameters used for the proposed system is reduced by 83.53% compared to the ResNet system. These results prove that the proposed system with the competing-words-based feature is highly effective at improving WUW recognition performance in noisy environments with a smaller footprint.

TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants

“…They artificially corrupt the original samples, which usually translates into better performance figures, making the models to be more robust with regard to a bigger variety of noises or unseen scenarios. Similar approaches can be seen across different speech tasks, such as in keyword spotting [18], in ASR [19,20], or in WUW detection [21]. Therefore, we adopt similar ideas for our training data employed by all the classifiers described in this work.…”

A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

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