Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.
Most existing SLR researches @cite_15 @cite_38 @cite_19 @cite_30 fall into the category of isolated SLR, i.e., recognition of words or expressions, similar to action recognition @cite_5 . A more challenging problem is the continuous SLR, which involves the reconstruction of sentence structures. Most existing continuous SLR methods divide the sentence-to-sentence recognition problem into three stages, temporal segmentation of videos, isolated word expression recognition (i.e., isolated SLR), and sentence synthesis with a language model. For example, DTW-HMM @cite_6 proposed a threshold matrix based coarse temporal segmentation step followed by a Dynamic Time Warping (DTW) algorithm and a bi-grammar model. In @cite_7 , a new HMM based language model is incorporated. Recently, transitional movements attract a lot of attention @cite_26 @cite_33 @cite_17 @cite_34 because they can serve as the basis for temporal segmentation.
@cite_30 @cite_38 @cite_26 @cite_33 @cite_7 @cite_6 @cite_19 @cite_5 @cite_15 @cite_34 @cite_17
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Recently, great progress has been made in sign language recognition. Most approaches are based on the Hidden Markov Model (HMM) with various features, such as motion trajectory. Recognition for sign sentences is obtained from optimal path by Viterbi algorithm, however, some wrong jumps are usually caused by transitional movements between signs. To address the problem, in this paper, we propose an approach consisting of two stages: offline training and online recognition. In the offline training stage, we propose a threshold matrix and rate thresholds. Each element of the threshold matrix describes the minimal probability when a segment belongs to a sign, and rate thresholds are defined as the average probability for signs. So, if certain segment's evaluation is smaller than all the thresholds, it is regarded as a transitional movement and then it should be removed. In the online recognition stage, coarse segmentation, based on the threshold matrix, records the time interval for fine segmentation, and fine segmentation, based on Dynamic Time Warping(DTW) and Length-Root method, determines the endpoint for each candidate sign and selects the most possible one. The final recognition is obtained by concatenating the most possible signs. We evaluate our approach with Kinect-based dataset and the experiments demonstrate the effectiveness of our approach. The major challenges that sign language recognition (SLR) now faces are developing methods that solve large-vocabulary continuous sign problems. In this paper, transition-movement models (TMMs) are proposed to handle transition parts between two adjacent signs in large-vocabulary continuous SLR. For tackling mass transition movements arisen from a large vocabulary size, a temporal clustering algorithm improved from k-means by using dynamic time warping as its distance measure is proposed to dynamically cluster them; then, an iterative segmentation algorithm for automatically segmenting transition parts from continuous sentences and training these TMMs through a bootstrap process is presented. The clustered TMMs due to their excellent generalization are very suitable for large-vocabulary continuous SLR. Lastly, TMMs together with sign models are viewed as candidates of the Viterbi search algorithm for recognizing continuous sign language. Experiments demonstrate that continuous SLR based on TMMs has good performance over a large vocabulary of 5113 Chinese signs and obtains an average accuracy of 91.9 In this paper we describe a system for the recognition and normalization of temporal expressions (Task 13: TempEval-2, Task A). The recognition task is approached as a classification problem of sentence constituents and the normalization is implemented in a rule-based manner. One of the system features is extending positive annotations in the corpus by semantically similar words automatically obtained from a large unannotated textual corpus. The best results obtained by the system are 0.85 and 0.84 for precision and recall respectively for recognition of temporal expressions; the accuracy values of 0.91 and 0.55 were obtained for the feature values type and val respectively. An unsupervised iterative approach for extracting a new lexicon (or unknown words) from a Chinese text corpus is proposed in this paper. Instead of using a non-iterative segmentation-merging-filtering-and-disambiguation approach, the proposed method iteratively integrates the contextual constraints (among word candidates) and a joint character association metric to progressively improve the segmentation results of the input corpus (and thus the new word list.) An augmented dictionary, which includes potential unknown words (in addition to known words), is used to segment the input corpus, unlike traditional approaches which use only known words for segmentation. In the segmentation process, the augmented dictionary is used to impose contextual constraints over known words and potential unknown words within input sentences; an unsupervised Viterbi Training process is then applied to ensure that the selected potential unknown words (and known words) maximize the likelihood of the input corpus. On the other hand, the joint character association metric (which reflects the global character association characteristics across the corpus) is derived by integrating several commonly used word association metrics, such as mutual information and entropy, with a joint Gaussian mixture density function; such integration allows the filter to use multiple features simultaneously to evaluate character association, unlike traditional filters which apply multiple features independently. The proposed method then allows the contextual constraints and the joint character association metric to enhance each other; this is achieved by iteratively applying the joint association metric to truncate unlikely unknown words in the augmented dictionary and using the segmentation result to improve the estimation of the joint association metric. The refined augmented dictionary and improved estimation are then used in the next iteration to acquire better segmentation and carry out more reliable filtering. Experiments show that both the precision and recall rates are improved almost monotonically, in contrast to non-iterative segmentation-merging-filtering-and-disambiguation approaches, which often sacrifice precision for recall or vice versa. With a corpus of 311,591 sentences, the performance is 76 (bigram), 54 (trigram), and 70 (quadragram) in F-measure, which is significantly better than using the non-iterative approach with F-measures of 74 (bigram), 46 (trigram), and 58 (quadragram).