The effectiveness of a high variability identification training procedure to improve native Japanese identification and production of the American English (AE) mid and low vowels /ae/, /A/, /2/, /O/, /Ç/ was investigated. Vowel identification and production performance for two groups of Japanese participants was measured before and after a 6-week identification training period. Recordings were made of both group's pre-/posttraining vowel productions of the five vowels, which were evaluated by a group of native AE listeners using a five-alternative, forced-choice identification task and by an acoustic analysis of the vowel productions. The overall results confirmed that the identification performance of the experimental (trained) participants improved after identification training with feedback and that the training also had a positive effect on their production of the target AE vowels. When learning a second or foreign language (L2), adults typically have difficulty mastering certain phonemic contrasts in the target language (Best, 1995;MacKain, Best, & Strange, 1981). As language-specific perceivers, adults' perception of speech is attuned to contrastive elements that serve to distinguish native phones during first or native language (L1) acquisition. It can be a challenge for listeners to accurately distinguish between sounds in the L2, or between L1 and

Language identification technology is widely used in the domains of machine learning and text mining. Many researchers have achieved excellent results on a few selected European languages. However, the majority of African and Asian languages remain untested. The primary objective of this research is to evaluate the performance of our new n-gram based language identification algorithm on 68 written languages used in the European, African and Asian regions. The secondary objective is to evaluate how n-gram orders and a mix n-gram model affect the relative performance and accuracy of language identification. The n-gram based algorithm used in this paper does not depend on the n-gram frequency. Instead, the algorithm is based on a Boolean method to determine the output of matching target n-grams to training n-grams. The algorithm is designed to automatically detect the language, script and character encoding scheme of a written text. It is important to identify these three properties due to the reason that a language can be written in different types of scripts and encoded with different types of character encoding schemes. The experimental results show that in one test the algorithm achieved up to 99.59% correct identification rate on selected languages. The results also show that the performance of language identification can be improved by using a mix n-gram model of bigram and trigram. The mix n-gram model consumed less disk space and computing time, compared to a trigram model.

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Human beings are fascinating creatures. Their behavior and appearance cannot be compared with any other living organism in the world. They have two distinct features with compared to any other living being; unique physical nature and emotions / feelings. Anybody who studies on humans or trying to construct human like machines should consider these two vital facts. When robots are interacting with humans and other objects, they certainly have a safe distance between them and the object. But how can this distance be optimized when interacting with humans; will there be any advantages over achieving this; will it help to improve the condition of robots; can it be a mere constant distance; how will the humans react, are some questions arosed. In order to "humanize" robots, they (robots) should also have certain understating of such emotions that we, humans have. In this research project, authors are trying to "teach" one such human understanding, commonly known as "personal space" to autonomous mobile robots.