トウケイテキ シュホウ オ モチイタ オンセイ シンゴウ ノ フクゲン シュホウ ノ カイリョウ

In recent years, IP telephone use has spread rapidly thanks to the development of VoIP (Voice over IP) technology. However, an unavoidable problem of the IP telephone is deterioration of speech due to packet loss, which often occurs on the wireless network. To overcome this problem, we propose a novel packet loss concealment algorithm using speech recognition and synthesis. This proposed method uses linguistic information and can deal with the lack of syllable units which conventional methods are unable to handle. We conducted subjective and objective evaluation experiments. These results showed the effectiveness of the proposed method. Although there is a processing delay in the proposed method, we believe that this method will open up new applications for speech recognition and speech synthesis technology

Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM

Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease. To detect eye motions, electrooculography (EOG) is used. For efficient communication, the input speed is critical. However, it is difficult for conventional EOG recognition methods to accurately recognize fast, sequentially input eye motions because adjacent eye motions influence each other. In this paper, we propose a context-dependent hidden Markov model- (HMM-) based EOG modeling approach that uses separate models for identical eye motions with different contexts. Because the influence of adjacent eye motions is explicitly modeled, higher recognition accuracy is achieved. Additionally, we propose a method of user adaptation based on a user-independent EOG model to investigate the trade-off between recognition accuracy and the amount of user-dependent data required for HMM training. Experimental results show that when the proposed context-dependent HMMs are used, the character error rate (CER) is significantly reduced compared with the conventional baseline under user-dependent conditions, from 36.0 to 1.3%. Although the CER increases again to 17.3% when the context-dependent but user-independent HMMs are used, it can be reduced to 7.3% by applying the proposed user adaptation method

Dealing with Acronyms in Biomedical Texts

Recently, there has been a growth in the amount of machine readable information pertaining to the biomedical field. With this growth comes a desire to be able to extract information, answer questions, etc. based on the information in the documents. Many of these desired tasks require sophisticated language processing algorithms, such as part-of-speech tagging, parsing, and semantic interpretation. In order to use these algorithms the text must first be cleansed of acronyms, abbreviations, and misspellings. In this paper we look at identifying, expanding, and disambiguating acronyms in biomedical texts. We present an integrated system that combines previously used methods for dealing with acronyms and Natural Language Processing techniques in new way for a new domain. The result is an integrated system that achieves a high precision and recall. We break the task up into three modular steps: Identification, Expansion, and Disambiguation. During identification, each word is examined to determine if it is an acronym or not. For this, a hybrid approach that is composed of a Naive Bayesian classifier and a set of handcrafted rules is used. We are able to achieve results of 99.96 % accuracy with a small training set. During the expansion step, a list of possible meanings for the words determined to be acronyms is created. We break the expansion up into two categories, local and global expansion. For local expansion we use windowing and longest common subsequence to generate the possible expansions. Global expansion requires an acronym database to retrieve the possible expansions. The disambiguation step takes the list of possible meanings and determines which meaning is the correct one. To disambiguate the different candidate expansions we use WordNet and semantic similarity. Overall we obtain a recall and precision of over 91%. Keywords: Acronyms, Text Cleansing, Bioinformatic

Category Classification and Topic Discovery of Japanese and English News Articles

AbstractThis paper presents algorithms for topic analysis of news articles. Topic analysis entails category classification and topic discovery and classification. Dealing with news has special requirements that standard classification approaches typically cannot handle. The algorithms proposed in this paper are able to do online training for both category and topic classification as well as discover new topics as they arise. Both algorithms are based on a keyword extraction algorithm that is applicable to any language that has basic morphological analysis tools. As such, both the category classification and topic discovery and classification algorithms can be easily used by multiple languages. Through experimentation the algorithms are shown to have high precision and recall in tests on English and Japanese