3,903 research outputs found
Intent recognition in smart living through deep recurrent neural networks
Electroencephalography (EEG) signal based intent recognition has recently
attracted much attention in both academia and industries, due to helping the
elderly or motor-disabled people controlling smart devices to communicate with
outer world. However, the utilization of EEG signals is challenged by low
accuracy, arduous and time- consuming feature extraction. This paper proposes a
7-layer deep learning model to classify raw EEG signals with the aim of
recognizing subjects' intents, to avoid the time consumed in pre-processing and
feature extraction. The hyper-parameters are selected by an Orthogonal Array
experiment method for efficiency. Our model is applied to an open EEG dataset
provided by PhysioNet and achieves the accuracy of 0.9553 on the intent
recognition. The applicability of our proposed model is further demonstrated by
two use cases of smart living (assisted living with robotics and home
automation).Comment: 10 pages, 5 figures,5 tables, 21 conference
Towards End-to-End spoken intent recognition in smart home
International audienceVoice based interaction in a smart home has become a feature of many industrial products. These systems react to voice commands, whether it is for answering a question, providing music or turning on the lights. To be efficient, these systems must be able to extract the intent of the user from the voice command. Intent recognition from voice is typically performed through automatic speech recognition (ASR) and intent classification from the transcriptions in a pipeline. However, the errors accumulated at the ASR stage might severely impact the intent classifier. In this paper, we propose an End-to-End (E2E) model to perform intent classification directly from the raw speech input. The E2E approach is thus optimized for this specific task and avoids error propagation. Furthermore, prosodic aspects of the speech signal can be exploited by the E2E model for intent classification (e.g., question vs imperative voice). Experiments on a corpus of voice commands acquired in a real smart home reveal that the state-of-the art pipeline baseline is still superior to the E2E approach. However, using artificial data generation techniques we show that significant improvement to the E2E model can be brought to reach competitive performances. This opens the way to further research on E2E Spoken Language Understanding
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