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Based on the principle of large margin classifier, recently we proposed two novel training methods, namely large margin estimation (LME) [8] and maximum relative margin estimation (MRME) [9] for speech recognition. In LME or MRME, HMM parameters are estimated to maximize the minimum margin among all training utterances. However their original formulation is limited to isolated-word ASR tasks. In this...
An active noise cancellation method using a MEMS accelerometer is developed for recovering corrupted wearable sensor signals due to body motion. The method is developed for a finger ring PPG sensor, the signal of which is susceptive to the hand motion of the wearer. A MEMS accelerometer (ACC) imbedded in the PPG sensor detects the hand acceleration, and is used for recovering the corrupted PPG signal...
In this paper, we propose a dynamic in-search discriminative training approach of a large-scale HMM model for large vocabulary speech recognition. A previously proposed data selection method is used to choose competing hypotheses dynamically during Viterbi beam search procedure. Particularly, all active word-ending paths are examined during search with reference transcription to identify competing...
In this paper we describe how discriminative training can be applied to language models for speech recognition. Language models are important to guide the speech recognition search, particularly in compensating for mistakes in acoustic decoding. A frequently used measure of the quality of language models is the perplexity; however, what is more important for accurate decoding is not necessarily having...
This paper presents kinematic analysis of the end-effector position errors of flexible robot manipulators. The analysis gives kinematic fundamentals for their trajectory control. The end-effector position errors due to the link flexibility cannot be controlled directly but can be compensated only indirectly by the joint angles or displacements. The compensation, however, is sometimes impossible if...
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