This paper proposes a novel PN (Pseudo Noise) synchronization system using Cycle-and-Add property of M-sequence featuring fast acquisition in DS-CDMA (direct sequence-code division multiple access). Fast acquisition is carried out by generating a PN sequence in a simple multiplicative action of a received signal with its delayed one. This multiplicative action is similar to differentially coherent detection and realizes an anti-fading property. Easy implementation is materialized by the fact that the system is mostly composed of baseband devices. The principle, performance evaluation and the detection probability of synchronization for the proposed method are shown. Furthermore, detection probability of synchronization in a fast Rayleigh fading channel is shown for the performance evaluation.

Recognition errors caused by out-of-vocabulary (OOV) words lead critical problems when developing spoken language understanding systems based on automatic speech recognition technology. And automatic vocabulary adaptation is an essential technique to solve these problems. In this paper, we propose a novel and effective automatic vocabulary adaptation method. Our method selects OOV words from relevant documents using combined scores of semantic and acoustic similarities. Using this combined score that reflects both semantic and acoustic aspects, only necessary OOV words can be selected without registering redundant words. In addition, our method estimates probabilities of OOV words using semantic similarity and a class-based N-gram language model. These probabilities will be appropriate since they are estimated by considering both frequencies of OOV words in target speech data and the stable class N-gram probabilities. Experimental results show that our method improves OOV selection accuracy and recognition accuracy of newly registered words in comparison with conventional methods.

We propose a continuous speech recognition algorithm utilizing island-driven A* search. Conventional left-to-right A* search is probable to lose the optimal solution from a finite stack if some obscurities appear at the start of an input speech. Proposed island-driven A* search proceeds searching forward and backward from the clearest part of an input speech, and thus can avoid to lose the optimal solution from a finite stack.

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.