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This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the low rank approximation of the original sign videos for two aspects. First, under the observations that most visual information of a sign sequence typically concentrates on limited key frames, we apply an online low rank approximation of sign videos for the first time to select the key frames. Second, rather...
This paper describes the TBNR system, which features at many state-of-the-art technologies of speech recognition, covering the decoder, acoustic modeling, speech recognition features, and etc. By integrating these technologies, several optimizations have been performed to utilize multi-processors resources. Along with models in several typical languages, these systems could be used at once for several...
This paper reports our recent work on optimizing the AF (articulatory features) based confidence measures, and combining them with the traditional HMM-based confidence measures. Different articulatory properties are analyzed using a separate AF-based confidence calculation method proposed in this paper, and are observed to be both complementary and redundant. A more compact subset is chosen and assembled...
Sign language recognition systems suffer from the problem of signer dependence. In this letter, we propose a novel method that adapts the original model set to a specific signer with his/her small amount of training data. First, affinity propagation is used to extract the exemplars of signer independent hidden Markov models; then the adaptive training vocabulary can be automatically formed. Based...
Word alignment plays a critical role in statistical machine translation (SMT) and cross-language information retrieval. Until now, most existing methods get the word alignment within the whole range of the sentence length. The alignment quality is unsatisfactory. In this paper, we propose a novel approach to word alignment based on multi-grain model (WAMG). We split a parallel sentence pair into blocks...
Sign Language Recognition (SLR) systems are mostly based on Hidden Markov Model (HMM) and have achieved excellent results. However, the assumption of frame independence in HMM makes it inconsistent with the characteristic of strong temporal correlation in sign language signals. Polynomial Segment Model (PSM) explicitly represents the temporal evolution of sign language features as a Gaussian process...
A segmentation-free strategy based on hidden Markov models (HMMs) is presented for offline recognition of unconstrained Chinese handwriting. As the first step, handwritten textlines are converted to observation sequence by sliding windows and character segmentation stage is avoided prior to recognition. Following that, embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best...
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