Yajima et al. proposed a method based on amplitude and phase coding of audio signals. This method has relatively high sound quality because human auditory property is considered for embedding. However, in this method, the tolerance to attacks tends to be weak. Hence, we propose a high-tolerance watermarking method using BCH code which is one of error correcting code. This paper evaluates whether our method preserves the sound quality while ensuring high tolerance.

We propose two methods to fuse auditory information and visual information for accurate sppech recognition. The first method fuses two kinds of information by using linear combination after calculating two kinds of probabilities by HMM for each word. The second method fuses two kinds of information by using the histogram which expresses the correlation of them. We have performed experiments comparing the proposed methods with the conventional method and confirmed the validity of the proposed methods.

This study proposes fuzzy matrix quantization (FMQ) which is a new coding technique developed to obtain discrete symbols employed for hidden Markov models (HMM's). FMQ is a coding technique combining fuzzy vector quantization with matrix quantization. The validity of FMQ is evaluated by a speaker-independent isolated word recognition task. First, the effect of FMQ is examined when FMQ is applied to the training phase and/or recognition phase. The effects of number of training data, codebook size and codeword matrix size for recognition accuracy are investigated. And the results of the speech recognition based on HMM recognizer using FMQ technique is compared with HMM recognizers using conventional quantization methods, vector quantization and fuzzy vector quantization. As a result, FMQ is the effective coding technique for isolated word recognition on condition that codebook size is large, above all, when FMQ is applied to the training phase and training data set is small.

This paper presents a correlation-based watermarking method for video using the similarity of adjacent frames. In general, the adjacent frames of a video sequence is very similar. In the proposed scheme, we use an adjoining frame in detection process instead of an original image in the watermarking scheme of Cox et al. So the proposed method does not need an original video sequence in detection process. When a watermarked video sequence is attacked by overwriting copy or frame dropping, the pair of the frames that is not adjoining in an original video sequence is used in detection process. However, since a watermark is embedded in a part of each frame and embedding positions are different for each frame in the proposed method, we can detect the watermark even from an overwriting-copied video sequence and a frame-dropped video sequence. Experimental results show that the proposed method is robust against overwriting copy and frame dropping. Moreover, it is shown from experimental results that the method has robustness to low bitrate MPEG compression and StirMark attack.