Power line communications (PLC) is a communication technology that uses a power-line as a transmission medium. Previous studies have shown that connecting an AC adapter such as a mobile phone charger to the power-line affects signal quality. Therefore, in this paper, the authors analyze the influence of chargers on inter-computer communications using packet capture to evaluate communications quality. The analysis results indicate the occurrence of a short duration in which packets are not detected once in a half period of the power-line supply: named communication forbidden time. For visualizing the communication forbidden time and for evaluating the communications quality of the inter-computer communications using PLC, the authors propose an instantaneous power-line frequency synchronized superimposed chart and its plotting algorithm. Further, in order to analyze accurately, the position of the communication forbidden time can be changed by altering the initial burst signal plotting position. The difference in the chart, which occurs when the plotting start position changes, is also discussed. We show analysis examples using the chart for a test bed data assumed an ideal environment, and show the effectiveness of the chart for analyzing PLC inter-computer communications.

This paper describes Japanese spoken sentence recognition using hybrid language modeling, which combines the advantages of both syntactic and stochastic language models. As the baseline system, we adopted the HMM-LR speech recognition system, with which we have already achieved good performance for Japanese phrase recognition tasks. Several improvements have been made to this system aimed at handling continuously spoken sentences. The first improvement is HMM training with continuous utterances as well as word utterances. In previous implementations, HMMs were trained with only word utterances. Continuous utterances are included in the HMM training data because coarticulation effects are much stronger in continuous utterances. The second improvement is the development of a sentential grammar for Japanese. The sentential grammar was created by combining inter- and intra-phrase CFG grammars, which were developed separately. The third improvement is the incorporation of stochastic linguistic knowledge, which includes stochastic CFG and a bigram model of production rules. The system was evaluated using continuously spoken sentences from a conference registration task that included approximately 750 words. We attained a sentence accuracy of 83.9% in the speaker-dependent condition.

In this paper, we propose an extended LR parsing algorithm, called LR parsing with a category reachability test (the LR-CRT algorithm). The LR-CRT algorithm enables a parser to efficiently recognize those sentences that belong to a specified grammatical category. The key point of the algorithm is to use an augmented LR parsing table in which each action entry contains a set of reachable categories. When executing a shift or reduce action, the parser checks whether the action can reach a given category using the augmented table. We apply the LR-CRT algorithm to improve a speech recognition system based on two-level LR parsing. This system uses two kinds of grammars, inter- and intra-phrase grammars, to recognize Japanese sentential speech. Two-level LR parsing guides the search of speech recognition through two-level symbol prediction, phrase category prediction and phone prediction, based on these grammars. The LR-CRT algorithm makes possible the efficient phone prediction based on the phrase category prediction. The system was evaluated using sentential speech data uttered phrase by phrase, and attained a word accuracy of 97.5% and a sentence accuracy of 91.2%

This paper describes a continuous speech recognition system using two-level LR parsing and phone based HMMs. ATR has already implemented a predictive LR parsing algorithm in an HMM-based speech recognition system for Japanese. However, up to now, this system has used only intra-phrase grammatical constraints. In Japanese, a sentence is composed of several phrases and thus, two kinds of grammars, namely an intra-phrase grammar and an inter-phrase grammar, are sufficient for recognizing sentences. Two-level LR parsing makes it possible to use not only intra-phrase grammatical constraints but also inter-phrase grammatical constraints during speech recognition. The system is applied to Japanese sentence recognition where sentences were uttered phrase by phrase, and attains a word accuracy of 95.9% and a sentence accuracy of 84.7%.

Current continuous speech recognition systems essentially ignore unknown words. Systems are designed to recognize words in the lexicon. However, for using speech recognition systems in a real application such as spoken-language processing, it is very important to process unknown words. This paper proposes a continuous speech recognition method which accepts any utterance that might include unknown words. In this method, words not in the lexicon are transcribed as phone sequences, while words in the lexicon are recognized correctly. The HMM-LR speech recognition system, which is an integration of Hidden Markov Models and generalized LR parsing, is used as the baseline system, and enhanced with the trigram model of syllables to take into account the stochastic characteristics of a language. In our approach, two kinds of grammars, a task grammar which describes the task and a phonetic grammar which describes constraints between phones, are merged and used in the HMM-LR system. The system can output a phonetic transcription for an unknown word by using the phonetic grammar. Experiment results indicate that our approach is very promising.

We have developed a hardware accelerator for Java platforms, integrated on a SuperH microprocessor core, using a 130-nm CMOS process. The Java accelerator, a bytecode translation unit (BTU), is tightly coupled with the CPU to share resources. The BTU supports 159 basic bytecodes and 5 or 6 optional bytecodes. It supports both connected device configuration (CDC) 1.0 and connected limited device configuration (CLDC) 1.0.4 technologies. The BTU corresponds to the dual-issued superscalar CPU and applies a new method, control-sharing. With this method, the BTU always grasps the pipeline status of the CPU, and the Java program is processed by both the BTU and the CPU. To implement this method, we developed some acceleration techniques: fast branch requests, enhanced CPU instructions, Java runtime exception detection hardware, and fewer overhead cycles of handover between the BTU and the CPU. In particular, the BTU can detect Java runtime exceptions in parallel with other processing, such as an array access. With previous methods, there is a disadvantage in that CPU efficiency decreases for Java-specific processing, such as array index bounds checking. The sample chip was fabricated in Renesas 130-nm, five-layer Cu, dual-vth low-power CMOS technology. The chip runs at 216 MHz and 1.2 V. The BTU has 75 kG. The benchmark on an evaluation board showed 6.55 embedded caffeine marks (ECM)/MHz on the CLDC 1.0.4 configuration, a tenfold speed increase without the BTU for roughly the same power consumption. In other words, power savings of 90 percent with the same performance were achieved.

This paper discusses three approaches for combining an efficient LR parser and phoneme-context-dependent HMMs and compares them through continuous speech recognition experiments. In continuous speech recognition, phoneme-context-dependent allophonic models are considered very helpful for enhancing the recognition accuracy. They precisely represent allophonic variations caused by the difference in phoneme-contexts. With grammatical constraints based on a context free grammar (CFG), a generalized LR parser is one of the most efficient parsing algorithms for speech recognition. Therefore, the combination of allophonic models and a generalized LR parser is a powerful scheme enabling accurate and efficient speech recognition. In this paper, three phoneme-context-dependent LR parsing algorithms are proposed, which make it possible to drive allophonic HMMs. The algorithms are outlined as follows: (1) Algorithm for predicting the phonemic context dynamically in the LR parser using a phoneme-context-independent LR table. (2) Algorithm for converting an LR table into a phoneme-context-dependent LR table. (3) Algorithm for converting a CFG into a phoneme-context-dependent CFG. This paper also includes discussion of the results of recognition experiments, and a comparison of performance and efficiency of these three algorithms.

The Sinclair scattering matrix is defined in a fixed radar range. If a radar target extends in the range direction, the reflected signal or the compound scattering matrix will undergo interaction of multiple reflections. Since scattering matrix is subject to target parameters such as shape, size, orientation, material, and radar parameters as frequency, polarization, and incidence angle, it is difficult to specify a representative scattering matrix of a general target. Therefore we choose the simplest target, wire, and its scattering matrix to examine the effect of targets aligned in the range direction with respect to the compound scattering matrix. First, we present a simple formula for the compound scattering matrix of wires with the phase difference due to spacing. Then, we employed the FDTD method to examine the scattering phenomena, changing the spacing in the range direction. The FDTD result reveals that two wires can become sphere (plate) and dihedral corner reflector (diplane) component generators; and that four wires can become a good helix component generator. These phenomena are verified with a laboratory measurement. From the result, the target decomposition should be carefully carried out in terms of range. If a range resolution of a radar is not high enough, the scattering matrix of the desired target may be affected by the targets behind.