In Taiwan, a Web Accessibility Service was e stablished in 2004, which provides online courses for adult learners with disabilities to acquire job-oriented skills, particularly computer skills. Although a large number of learners have registered for the courses, the percentage of learners who complete their study is rather low. The purpose of this study is to identify issues related to the current services through examining the online learning activities of its participants in the 2012 term. Data sources consist of discussion statements posted online, discourse of online meetings, and self-reported questionnaires. The results indicate that the Service support team was rather dedicated to maintaining the system and courses offered. The learners who submitted the online questionnaires also appeared rather satisfied with the overall services provided. It seems that the low study completion rate, 5.4% in 2012, may not be due to the services per se. Other factors causing the low completion rate must be further explored. Suggestions of future directions for improving the services are provided.

Imperfect speech recognition often leads to degraded performance when exploiting conventional text-based methods for speech summarization. To alleviate this problem, this paper investigates various ways to robustly represent the recognition hypotheses of spoken documents beyond the top scoring ones. Moreover, a summarization framework, building on the Kullback-Leibler (KL) divergence measure and exploring both the relevance and topical information cues of spoken documents and sentences, is presented to work with such robust representations. Experiments on broadcast news speech summarization tasks appear to demonstrate the utility of the presented approaches.

Speech summarization, distilling important information and removing redundant and incorrect information from spoken documents, has become an active area of intensive research in the recent past. In this paper, we consider hybrids of supervised and unsupervised models for extractive speech summarization. Moreover, we investigate the use of the unsupervised summarizer to improve the performance of the supervised summarizer when manual labels are not available for training the latter. A novel training data selection and relabeling approach designed to leverage the inter-document or/and the inter-sentence similarity information is explored as well. Encouraging results were initially demonstrated. Index Terms — Speech summarization, hybrid summarizer, unsupervised training

The performance of current automatic speech recognition (ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources. Several methods have been proposed to improve ASR robustness over the last few decades. The related literature can be generally classified into two categories according to whether the methods are directly based on the feature domain or consider some specific statistical feature characteristics. In this paper, we present a polynomial regression approach that has the merit of directly characterizing the relationship between speech features and their corresponding distribution characteristics to compensate for noise interference. The proposed approach and a variant were thoroughly investigated and compared with a few existing noise robustness approaches. All experiments were conducted using the Aurora-2 database and task. The results show that our approaches achieve considerable word error rate reductions over the baseline system and are comparable to most of the conventional robustness approaches discussed in this paper.

The performance of current automatic speech recognition (ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources. Quite a few of techniques have been proposed to improve ASR robustness over the last few decades. Related work reported in the literature can be generally divided into two aspects according to whether the orientation of the methods is either from the feature domain or from the corresponding probability distributions. In this paper, we present a polynomial regression approach which has the merit of directly characterizing the relationship between the speech features and their corresponding probability distributions to compensate the noise effects. Two variants of the proposed approach are also extensively investigated as well. All experiments are conducted on the Aurora-2 database and task. Experimental results show that for clean-condition training, our approaches achieve considerable word error rate reductions over the baseline system, and also significantly outperform other conventional methods.

With the rapid development of intelligent transportation systems (ITS), how to provide users with a natural and efficient human-machine interface is now becoming a crucial issue for driver safety. It is no doubt that speech will be one of the best mediators of human-machine interaction; however, the performance of automatic speech recognition (ASR) always radically degrades when the input speech is corrupted by varying noises. In this paper, we consider the use of histogram equalization (HEQ) for robust ASR. A novel data fitting scheme was presented to efficiently approximate the inverse of the cumulative density function of training speech for HEQ, which has the merits of lower storage and time consumption compared to the conventional table-lookup or quantile based HEQ approaches. Moreover, a more elaborate attempt of using multiple inverse functions for different noise conditions was investigated as well. All experiments were carried out on the Aurora-2 standard database and task. Very encouraging results were obtained. The proposed robustness technique has also been properly integrated into our prototype system for in-vehicle traffic information retrieval using spoken queries.

Noise robustness is one of the primary challenges facing most automatic speech recognition (ASR) systems. A vast amount of research efforts on preventing the degradation of ASR performance under various noisy environments have been made during the past several years. In this paper, we consider the use of histogram equalization (HEQ) for robust ASR. In contrast to conventional methods, a novel data fitting method based on polynomial regression was presented to efficiently approximate the inverse of the cumulative density functions of speech feature vectors for HEQ. Moreover, a more elaborate attempt of using such polynomial regression models to directly characterizing the relationship between the speech feature vectors and their corresponding probability distributions, under various noise conditions, was proposed as well. All experiments were carried out on the Aurora-2 database and task. The performance of the presented methods were extensively tested and verified by comparison with the other methods. Experimental results shown that for cleancondition training, our method achieved a considerable word error rate reduction over the baseline system, and also significantly outperformed the other methods. Index Terms: noise robustness, speech recognition, histogram equalization, polynomial regression model

The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In the paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling structures and learning approaches were extensively investigated. In addition, the summarization capabilities were verified by comparison with the conventional vector space model and latent semantic indexing model, as well as the HMM model. The experiments were performed on the Chinese broadcast news collected in Taiwan. Noticeable performance gains were obtained.

The performance of current automatic speech recognition (ASR) systems radically deteriorates when the input speech is corrupted by various kinds of noise sources. Quite a few of techniques have been proposed to improve ASR robustness in the past several years. Histogram equalization (HEQ) is one of the most efficient techniques that have been used to compensate the nonlinear distortion. In this paper, we explored the use of the data fitting scheme to efficiently approximate the inverse of the cumulative density function of training speech for HEQ, in contrast to the conventional tablelookup or quantile based approaches. Moreover, the temporal average operation was also performed on the feature vector components to alleviate the influence of sharp peaks and valleys that were caused by non-stationary noises. Finally, we also investigated the possibility of combining our approaches with other feature discrimination and decorrelation methods. All experiments were carried out on the Aurora-2 database and task. Encouraging results were initially demonstrated. Index Terms: histogram equalization, data fitting, temporal average, robustness

Benes networks are nonblocking rearrangeable networks that can realize arbitrary permutations without conflict. A class of 2 log/sub b/ N stage networks which are equivalent to the Benes networks is introduced. Networks in this class can be either symmetric or asymmetric in their structure, regular or irregular in their interstage connections, and even, 2 log/sub b/ N or 2 log/sub b/ N-1 stages. All networks in this class are nonblocking rearrangeable. A switch labeling scheme is proposed to provide testing of the topological and functional equivalency for this class of networks. This switch labeling scheme can also provide a novel matrix representation for network configuration. This representation introduces a portability concept for the routing scheme on this class of networks. With this representation, a general routing scheme is also developed. This routing scheme can realize arbitrary permutation for the whole class of 2 log/sub b/ N stage networks.>