Improved model adaptation approach for recognition of reduced-frame-rate continuous speech.

In distributed speech recognition applications, the front-end device that stands for any handheld electronic device like smartphones and personal digital assistants (PDAs) captures the speech signal, extracts the speech features, and then sends the speech-feature vector sequence to the back-end server for decoding. Since the front-end mobile device has limited computation capacity, battery power and bandwidth, there exists a feasible strategy of reducing the frame rate of the speech-feature vector sequence to alleviate the drawback. Previously, we proposed a method for adjusting the transition probabilities of the hidden Markov model to enable it to address the degradation of recognition accuracy caused by the frame-rate mismatch between the input and the original model.

Discrete element modeling of a mining-induced rock slide.

Slopes are subjected to stress redistributions during underground mining activities, and this may eventually cause deformation or landslide. This paper takes Madaling landslide in Guizhou Province, China as a case study to investigate the failure mechanism and its run-out behaviours by using discrete element method. Previous qualitative analysis indicated that the slope experienced four stages of failure mechanisms: (1) development of tension cracks, (2) development of stepped-like creep cracks, (3) development of potential rupture surfaces, and (4) occurrence of the landslide.

High-order hidden Markov model for piecewise linear processes and applications to speech recognition.

The hidden Markov models have been widely applied to systems with sequential data. However, the conditional independence of the state outputs will limit the output of a hidden Markov model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a high-order hidden Markov model for piecewise linear processes is proposed to better approximate the behavior of a real process.

Model adaptation method for recognition of speech with missing frames.

In distributed speech recognition (DSR), data packets may be lost over error prone channels. A commonly used approach to rectify this is to reconstruct a full frame rate data sequence for recognition using linear interpolation. In this study, an error-concealment decoding method that dynamically adapts the transition probabilities of hidden Markov models to match the frame loss observation sequence is proposed.

Adaptation of hidden Markov models for recognizing speech of reduced frame rate.

The frame rate of the observation sequence in distributed speech recognition applications may be reduced to suit a resource-limited front-end device. In order to use models trained using full-frame-rate data in the recognition of reduced frame-rate (RFR) data, we propose a method for adapting the transition probabilities of hidden Markov models (HMMs) to match the frame rate of the observation. Experiments on the recognition of clean and noisy connected digits are conducted to evaluate the proposed method.