This paper presents a new approach to online recognition of handwritten Kanji characters focusing on their hierarchical structure. Stochastic context-free grammar (SCFG) is introduced to represent the Kanji character generating process in combination with Hidden Markov Models (HMM) representing Kanji substrokes and to improve the recognition accuracy of important and frequently used Kanji characters in which inter-stroke relative positions play important roles. Combining the stroke likelihood and the relative-position likelihood between character-parts in the parsing process is expected to compensate their ambiguities. By modeling relative positions and share the models across distinct Kanji categories, a small training data can yield effective results and enables us to recognize Kanji simply by defining the SCFG rules to represent their structures without training data. Experimental results on an online handwritten Kanji database from JAIST (Japan Advanced Institute of Science and Technology) showed significant improvements in the recognition rates of some important Kanji with relatively fewer strokes and also showed little difference between the trained-and the non-trained Kanji in recognition rates.
We work on automatic Japanese sign Language (JSL) recognition using Hidden Markov Model (HMM). An important issue for modeling sign is that how to determine the constituent element of sign (i.e., subunit) like "phoneme" in spoken language. We focused on special feature of sign language that JSL is composed of three types of phonological elements which is hand local information, position, and movement. In this paper, we propose an efficiently method of generating subunit using multi-stream HMM which is correspond to phonological elements. An isolated word recognition experiment has confirmed the effectiveness of our proposed method.
We present an algorithm for song composition using prosody of Japanese lyrics. Since Japanese is a "pitch accent" language, listener's apprehension is strongly affected by the pitch motions of the speaker. For example, the meaning of Japanese word "ha-shi" changes with the pitch. It means "bridge" with an upward pitch motion, and "chopsticks" with the motion inversed. A melody attached to the lyrics cause an effect similar to the pitch accent. Therefore we can assume that pitches of Japanese lyrics give constraints on pitch motions of the melody. Furthermore, chord progression, rhythm and accompaniment give constraints on the transitions and occurrences of the melody notes. If a certain melody for the lyrics were obtained, the melody would satisfy these constraints. Conversely, we can compose a song by finding the melody which optimally meets the condition. Implementation and Experimental ResultsOrpheus is an automatic composition system that we implemented using melody composition algorithm based on prosody. This system computes melody from the lyrics input with choices of chord progressions, rhythm patterns, and accompaniment instruments. We used Galatea-Talk[4] text-to-speech engine to analyze the prosody of Japanese lyrics, and HMM singing voice synthesizer[5] to generate the vocal part. We also implemented the system as a web-based application 1 . We did two experiments to evaluate the system. Firstly, we asked a classical music composer to evaluate 59 generated songs in five-grade evaluation. Secondly, we uploaded our system to get comments from a large number of users on the internet. During a year of operation, about 56,000 songs were generated by the users and 1378 people answered the questions about Orpheus and the generated songs. The results are shown in Fig. 1 and Fig. 2. Judging from the results, about 70.8% commented that the generated songs are attractive, and 84.9% of the users had fun trying this system.
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