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The authors present some experiments that show the capabilities of using recurrent neural networks (RNNs) in conjunction with hidden Markov models (HMMs) in the context of keyword spotting (KWS): the automatic recognition of a small set of keywords as they occur in unconstrained speech and/or noise. KWS is usually
This paper presents a robust keyword detection system for criminal scene analysis. The system follows the classical keyword spotting framework. A universal background model is designed and served as the filler model and anti-word model in keyword recognition and verification, respectively. Specifically, we analyze the
This paper presents a system for keyword detection in spontaneous speech. Keywords are predefined through a set of acoustic examples provided by the users. Keyword detection proceeds in two steps: keyword searching and verification. To address the problem of using the same phoneme models in both keyword and filter
This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional
-grain sparsification (CGS), introduces hardware-aware sparsity during the DNN training, which leads to efficient weight memory compression and significant computation reduction during classification without losing accuracy. We apply the proposed approach to DNN design for keyword detection and speech recognition. When the
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