In this letter, we propose a novel supervised pre-training technique for deep neural network (DNN)-hidden Markov model systems to achieve robust speech recognition in adverse environments. In the proposed approach, our aim is to initialize the DNN parameters such that they yield abstract features robust to acoustic environment variations. In order to achieve this, we first derive the abstract features from an early fine-tuned DNN model which is trained based on a clean speech database. By using the derived abstract features as the target values, the standard error back-propagation algorithm with the stochastic gradient descent method is performed to estimate the initial parameters of the DNN. The performance of the proposed algorithm was evaluated on Aurora-4 DB, and better results were observed compared to a number of conventional pre-training methods.

In this letter, we propose a novel approach to human activity recognition. We present a class of features that are robust to the tilt of the attached sensor module and a state transition model suitable for HMM-based activity recognition. In addition, postprocessing techniques are applied to stabilize the recognition results. The proposed approach shows significant improvements in recognition experiments over a variety of human activity DB.

In this letter, we propose a novel approach to estimate three different kinds of phone mismatch penalty matrices for two-stage keyword spotting. When the output of a phone recognizer is given, detection of a specific keyword is carried out through text matching with the phone sequences provided by the specified keyword using the proposed phone mismatch penalty matrices. The penalty matrices associated with substitution, insertion and deletion errors are estimated from the training data through deliberate error generation. The proposed approach has shown a significant improvement in a Korean continuous speech recognition task.