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

The purpose of this paper is to design an efficient recurrent neural network (RNN)-based speech recognition system using software with long short-term memory (LSTM). The design process involves speech acquisition, pre-processing, feature extraction, training and pattern recognition tasks for a spoken sentence recognition system using LSTM-RNN. There are five layers namely, an input layer, a fully connected layer, a hidden LSTM layer, SoftMax layer and a sequential output layer. A vocabulary of 80 words which constitute 20 sentences is used. The depth of the layer is chosen as 20, 42 and 60 and the accuracy of each system is determined. The results reveal that the maximum accuracy of 89% is achieved when the depth of the hidden layer is 42. Since the depth of the hidden layer is fixed for a task, increased performance can be achieved by increasing the number of hidden layers.

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Natural Language Processing (NLP) systems involve Natural Language Understanding (NLU), Dialogue Management (DM) and Natural Language Generation (NLG). The purpose of this work involves integrating learning with examples and rule-based processing to design an NLP system. The design involves a three-stage processing framework, which combines syntactic generation, semantic extraction and a strong rule-based control. The syntactic generator generates syntax by aligning sentences with Part-of-Speech (POS) tags limited by the number of words in the lexicon. The semantic extractor extracts meaningful keywords from the queries raised. The above two modules are controlled by generalized rules by the rule-based controller module. The system is evaluated under different domains. The results reveal that the accuracy of the system is 92.33% on an average. The design process is simple, and the processing time is 2.12 seconds, which is minimal compared to similar statistical models. The performance of an NLP tool in a certain task can be estimated by the quality of its predictions on the classification of unseen data. The results reveal similar performance with existing systems indicating the possibility of usage for similar tasks. The system supports a vocabulary of about 700 words and can be used as an NLP module in a spoken dialogue system for various domains or task areas.