The purpose of this work is to develop a spoken language processing system for smart device\ntroubleshooting using human-machine interaction. This system combines a software Bidirectional\nLong Short Term Memory Cell (BLSTM)-based speech recognizer and a hardware LSTM-based\nlanguage processor for Natural Language Processing (NLP) using the serial RS232 interface. Mel\nFrequency Cepstral Coecient (MFCC)-based feature vectors from the speech signal are directly\ninput into a BLSTM network. A dropout layer is added to the BLSTM layer to reduce over-fitting and\nimprove robustness. The speech recognition component is a combination of an acoustic modeler,\npronunciation dictionary, and a BLSTM network for generating query text, and executes in real time\nwith an 81.5% Word Error Rate (WER) and average training time of 45 s. The language processor\ncomprises a vectorizer, lookup dictionary, key encoder, Long Short Term Memory Cell (LSTM)-based\ntraining and prediction network, and dialogue manager, and transforms query intent to generate\nresponse text with a processing time of 0.59 s, 5% hardware utilization, and an F1 score of 95.2%.\nThe proposed system has a 4.17% decrease in accuracy compared with existing systems. The existing\nsystems use parallel processing and high-speed cache memories to perform additional training, which\nimproves the accuracy. However, the performance of the language processor has a 36.7% decrease in\nprocessing time and 50% decrease in hardware utilization, making it suitable for troubleshooting\nsmart devices.
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