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Artifact suppression and analysis of brain activities with electroencephalography signals

Neural Regeneration Research 2013年第16期8卷 1500-1513页

作者： Md. Rashed-Al-Mahfuz Md. Rabiul Islam keikichi hirose Md. Khademul Islam Molla Department of Computer Science and Engineering Pabna University of Science and Technology Department of Information and Communication Engineering The University of Tokyo Department of Computer Science and Engineering Rajshahi University

Brain-computer interface is a communication system that connects the brain with computer （or other devices） but is not dependent on the normal output of the brain （i.e., peripheral nerve and muscle）. Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

关键词： neural regeneration brain activity brain waves data adaptive filtering electroencephalography electro-oculogram artifact topographic mapping Wiener filtering neuroregeneration

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RAPID ADAPTATION OF N-GRAM LANGUAGE MODELS USING INTER-WORD CORRELATION FOR SPEECH RECOGNITION

RAPID ADAPTATION OF N-GRAM LANGUAGE MODELS USING INTER-WORD ...

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6～(th) International Conference on Spoken Language Processing

作者： Koki Sasaki keikichi hirose Department of Information and Communication Engineering University of Tokyo. Hongo 7-3-1Bunkyu-kuTokyo 113-0033Japan Department of Information and Communication Engineering University of Tokyo. Hongo 7-3-1Bunkyu-kuTokyo 113-0033Japan

＜正＞In this paper, we study the fast adaptation problem of n-gram language model under the MAP estimation framework. We have proposed a heuristic method to explore inter-word correlation to accelerate MAP adaptation of n-gram model. According to their correlations, the occurrence of one word can be used to predict all other words in adaptation text. In this way, a large n-gram model can be efficiently adapted with a small amount of adaptation data. The proposed fast adaptation approach is evaluated in a Japanese newspaper corpus. We have observed a signiticant perplexity reduction even when we have only several hundred adaptation sentences.

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MODELING AND GENERATION OF ACCENTUAL PHRASE F0 CONTOURS BASED ON DISCRETE HMMs SYNCHRONIZED AT MORAUNIT TRANSITIONS

MODELING AND GENERATION OF ACCENTUAL PHRASE F0 CONTOURS BASE...

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6～(th) International Conference on Spoken Language Processing

作者： Atsuhiro Sakurai Koji Iwano keikichi hirose Tsukuba R&D Center Texas Instruments Japan 7 Miyukigaoka Tsukuba Ibaraki 305-0841 Japan Dept. of Information and Communication Engineering Univ. of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Dept. of Information and Communication Engineering Univ of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Currently with Tokyo Institute of Technology

We propose a data-driven approach to intonation modeling and generation based on discrete Hidden Markov Models (HMM), where state transitions are synchronized with Japanese rhythmic units called morae. Mora-unit F contours are encoded using symbols that consist of two codes: the first is an index to a table of stylized mora F contours, and the second points to a table of quantized differences of the average F contour with respect to the previous mora. Both codebooks contain 32 codes. The HMM is used in generation mode, i.e., it generates a sequence of symbols for an intonational phrase without anyinput other than the length of the sequence, using a variation of Viterbi search with a modified distance function, in the training phase, the speech database is subdivided into classes according to the attributes of the target accentual phrase, and each class is associated to an HMM. After the output symbol sequence is generated, the F contour is constructed using the codebooks and a mora duration pattern. Evaluation experiments show that the HMMs are able to correctly produce F contours that reflect their training conditions.

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Dialect-Based Speaker Classification Using Speaker-Invariant Dialect Features

Dialect-Based Speaker Classification Using Speaker-Invariant...

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作者： Xuebin Ma Ruiyuan Xu Nobuaki Minematsu Yu Qiao keikichi hirose Aijun Li Graduate School of Frontier Sciences The University of Tokyo Institute of linguistics Chinese Academy of Social Sciences Graduate School of Information Science and Technology The University of Tokyo ShenZhen Institute of Advanced Technology Chinese Academy of Science

In our previous works, a structural pronunciation representation was proposed to extract the linguistic features from dialect pronunciation and classify speakers based on their dialects. In this paper, in order to prove that the structural method can extract the purely speaker-invariant dialectal features, several new experiments are carried out. First, using the data of 19 speakers from different dialect and sub-dialect regions, a dialect-based speaker classification experiment is carried out and satisfactory result is achieved. Then, one Chinese dialectologist transcribes all the data and reads the linguistic content of each original utterance in her voice through looking at the transcript and listening to the original utterance. So a new data set with minimum speaker differences(fixed speaker identity) is created. Using the new data, similar classification experiment is carried out and the result is very similar to the result of last experiment. It means that our method can extract the purely speaker-invariant dialectal features and classify speakers based on their dialects very well. After that, for the original and mimicked data sets, data sets with maximum speaker differences are simulated using high-quality voice morphing techniques. Using the original dialect data and the simulated versions together, classification experiments are carried out based two criteria, spectral comparison and structural comparison. By comparing these results, we can find that unlike the method of spectral comparison, the structural method can purely classify speakers based on their dialects, which shows the proposed dialect structures are speaker-independent and linguistic enough features.

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普通话作为二语的节奏模式的实验研究

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南京师范大学文学院学报 2014年第3期 169-175页

作者：顾文涛刘薛园廣瀬啓吉南京师范大学语言信息科技研究中心江苏南京210097 東京大学院情报理工学系日本东京113-8656

节奏特征对于语音的自然度有直接影响。针对普通话语音,本文比较了普通话母语者、母语为粤语的二语学习者、母语为英语的二语学习者的节奏模式。研究从含有轻声的孤立词开始,重点考察连续语流,通过11种时长指标的测量从客观上分析节奏... 详细信息

节奏特征对于语音的自然度有直接影响。针对普通话语音,本文比较了普通话母语者、母语为粤语的二语学习者、母语为英语的二语学习者的节奏模式。研究从含有轻声的孤立词开始,重点考察连续语流,通过11种时长指标的测量从客观上分析节奏模式。针对连续语流的研究发现,基于语速无关的或者语速归一化的时长指标,作为二语的普通话显示出与母语普通话非常接近的节奏模式;而基于未做语速归一化处理的时长指标,作为二语的普通话则向重音节拍模式的方向迁移,与说话人母语的节奏模式无关。这一结果,与孤立词情形下的结果并不一致,也违反了母语迁移效应的一般假设,但是与我们的听感基本吻合,其原因可以用二语与母语的语速差异、以及韵律结构变化带来的停延频次的差异来解释。

关键词：节奏时长指标二语语音普通话粤语英语

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