Yasuhiro Ogoshi

Kana-to-kanji conversion method using Markov chain model of words in bunsetsu

We previously proposed a kana-to-kanji conversion method of non-segmented kana sentences by using Markov chain model of words in sentence. However, we could not obtain the enough accuracy rate for conversion by this method. The cause is considered that the total number of the rules is not saturated in the dictionary of Markov chain probabilities of words in sentence. Therefore, we take notice that the total number of the rules is almost saturated in the dictionary of Markov chain probabilities of words in bunsetsu. In this paper, we propose a new kana-to-kanji conversion method by using this Markov chain model. That is, the new proposed method detects simultaneously the boundaries of kana bunsetsu in sentence and the boundaries of kana word in bunsetsu by using Markov chain model of kana words in bunsetsu, and then converts kana words to the candidates of kanji-kana word and selects the maximum likely candidate by using Markov chain model of kanji-kana words in bunsetsu. Through the experiments by using statistical data of daily Japanese newspaper, the previous proposed method (called Method-B1) and the new proposed method (called Method-B2) are evaluated by the criteria of the accuracy rate for conversion. From the results of the experiments, it is concluded that Method-B2 is superior to Method-B1 in the accuracy rate for conversion and is effective in kana-to-kanji conversion of non-segmented kana sentences.

Fostering the Development of Inclusively Minded Engineers

We developed an internship program with a special subsidiary company in order to educate engineers with an inclusive mind. A participating student has designed and manufactured equipment for workers with disabilities during the internship. We found that the student learned relations between major subjects at engineering and production techniques through interviews. In addition, we also considered the program is related to knowledge management. This suggests that the internship program with a special subsidiary company is one possible way for growing inclusive minded engineers.

Training System for the Expiration Training

For a speech therapist, the breathing method is one of the important techniques to train a person who has difficulty in speaking. Insufficient number of therapists makes it difficult to apply these breath training methods widely. Therefore, development of a new breath training method will be mandatory. With this reason, a new training system is developed and described in this paper. In this new method, expiration pattern of a speech therapist analyzing through an air flow sensor is constructed into visual graphic image in order to recognize expiration pattern easily. With experimental use of this system to without handicapped trainees, they could successfully imitate trainers pronunciation easily. A person who has difficulty in speaking will be able to learn easily how to do expiration for speaking through visual pattern.

A New Method to Reduce the Ambiguity of Japanese Phoneme Candidates Recognized by Recurrent Neural Networks

Up to now, the method to reduce the ambiguity of phoneme recognition using 2nd-order Markov chain model of phonemes, has been proposed and has been evaluated by phonem lattice simulated and limited to substitution error. However, the method will be necessary to demonstrate the effectiveness for the phoneme candidate lattice obtained by actual speech recognition devices. This paper deals with recurrent neural networks(RNN) which are well- suited for natural language processing of speech recognition, specially for phoneme recognition. The ability of these networks has been investigated by phoneme recognition experiments using a number of Japanese words uttered by a native male speaker in a quiet environment. A method to detect the locations of devoicing vowels using the short- time average energy has been also proposed, and evaluated. Form results of the experiments, it is shown that recognition rates achieved with RNN are higher than those obtained with conventional non-recurrent neural networks, and that the method to detect the locations of devoicing vowels is useful.

An Experimental System to Analyze or Synthesize Baby's Emotional Voice using the Varidation of Pitch Frequencies

It has been expected to utlize the technology of speech recognition and speech synthesis in order to improve the human interface. However, it is a difficult problem to extract and analyze emotional information contained in human voice in speech recognition and speech synthesis. This paper describes an experimental system to analyze and synthesize babys emotional voice using the perturbation parameters of pitch frequencies in order to improve human interface. The system is composed of three parts: the first part is to record and analyze the voice and to indicate the perturbation of pitch frequencies in real-time. The next part is to evaluate four perturbation parameters of pitch frequency for babys voice. Finally, babys voice is synthesized using 2-mass model of vocal cords vibration. From the experiment of several babys voices, it is shown that the proposed method is useful to analyze and synthesize babys emotional voice.