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Spoken Term Detection (STD) that considers the out-of-vocabulary (OOV) problem has generated significant interest in the field of spoken document processing. This study describes STD with false detection control using phoneme transition networks (PTNs) derived from the outputs of multiple speech recognizers. PTNs are similar to subword-based confusion networks (CNs), which are originally derived from a single speech recognizer. Since PTNformed index is based on the outputs of multiple speech recognizers, it is robust to recognition errors. Therefore, PTN should also be robust to recognition errors in an STD task, when compared to the CN-formed index from a single speech recognition system. Our PTN-formed index was evaluated on a test collection. The experiment showed that the PTN-based approach effectively detected OOV terms, and improved the F-measure value from 0.370 to 0.639 when compared with a baseline approach. Furthermore, we applied two false detection control parameters, one is based on the majority voting scheme. The other is a measure of the ambiguity of CN, to the calculation of detection score. By introducing these parameters, the performance of STD was found to be better (0.736 for the F-measure value) than that without any parameters (0.639).

Longzhi 2019. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes.Abstract. Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users' activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users' particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users' varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.

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This paper proposes to apply machine learning techniques to the task of combining outputs of multiple LVCSR models. The proposed technique has advantages over that by voting schemes such as ROVER, especially when the majority of participating models are not reliable. In this machine learning framework, as features of machine learning, information such as the model IDs which output the hypothesized word are useful for improving the word recognition rate. Experimental results show that the combination results achieve a relative word error reduction of up to 39 % against the best performing single model and that of up to 23 % against ROVER. We further empirically show that it performs better when LVCSR models to be combined are chosen so as to cover as many correctly recognized words as possible, rather than choosing models in descending order of their word correct rates.

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This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.