The articles in this special issue focus on the topics of the key technologies and applications that support wireless power transmission.

A software wave receiver was aboard the SS-520-2 rocket as a part of the plasma wave analyzer and successfully accomplished waveform observations and spectral observations. In the present paper, we describe the specifications and roles of the software wave receiver on the SS-520-2 rocket experiment. This receiver consists of a waveform receiver using real-time data compression and a spectral receiver with high time and frequency resolution using a programmable down converter. We report here on the first flight test of the new plasma wave receiver to be used for future planet explorers and space observation missions. Every 0.5 s, spectra of a 3-MHz signal with 0.3-kHz resolution are obtained, and the data compression of waveforms with the bandwidth of 15 kHz are performed. Although the sweep time was occasionally affected if the data were not compressed enough, no data were lost during the flight.

This paper presents a simple and efficient time domain technique to estimate an all-pole model on the mel-frequency scale (mel-LPC), and compares the recognition performance of the mel-LPC cepstrum with those of both the standard LPC mel-cepstrum and the MFCC (mel-frequency cepstral coefficient) through the Japanese dictation system (Julius) with 20,000 word vocabulary. First, the optimal value of the frequency warping factor is examined in terms of monosyllable accuracy. When using the optimal warping factors, the mel-LPC cepstrum attains word accuracies of 93.0% for male speakers and 93.1% for female speakers, which are 2.1% and 1.7% higher than those of the LPC mel-cepstrum, respectively. Furthermore, this performance is slightly superior to that of MFCC.

This paper examines the effectiveness of a generalized dynamic cepstrum in distant speech recognition. The generalized dynamic cepstrum (DyMFGC) is based upon the forward masking on the generalized logarithmic spectrum instead of the log-spectrum, which intends to make it robust to additive noise as well as convolutional noise. Digit recognition tests were carried out in a relatively quiet and small sized office environment. Under white noise environments, the DyMFGC outperforms the dynamic cepstrum on the logarithmic spectrum and the MFCC with cepstral mean normalization. It also maintains the word accuracy of 90% to 95% within a 1m distance from a source. In speech babble noise environments, the performance of the DyMFGC is approximately the same as that of the dynamic cepstrum on the logarithmic amplitude scale.

The Bayesian Learning approach (MAP Maximum A Posteriori) can be used for the incremental training of Continuous Density Hidden Markov Models (CDHMM), performed through speech data collected in real applications. The effectiveness of MAP is heavily conditioned by the correct balance between the a-priori knowledge and the field training data. In this paper we propose and evaluate several optimization methods of the MAP combination function, based either on maximum likelihood (ML) and heuristics criteria. To adjust the relevance of the a-priori knowledge we use the exponential forgetting technique into the MAP framework. We present several tests that compare the error rate reduction as a function of the selected optimization method and of the size of adaptation data.

This paper proposes a simple and efficient time domain technique to estimate an all-pole model on a mel-frequency axis (Mel-LPC), i.e., a bilinear transformed all-pole model by Strube. Autocorrelation coefficients on the mel-frequency axis are exactly derived by computing cross-correlation coefficients between speech signal and all-pass filtered one without any approximation. This method requires only two-fold computational cost as compared to conventional linear prediction analysis. The recognition performance of mel-cepstral parameters obtained by the Mel LPC analysis is compared with those of conventional LP mel-cepstra and the mel-frequency cepstrum coefficients (MFCC) through gender-dependent phoneme and word recognition tests. The results show that the Mel-LPC cepstrum attains a significant improvement in recognition accuracy over conventional LP mel-cepstrum, and gives slightly higher accuracy for male speakers and slightly lower accuracy for female speakers than MFCC.

This paper proposes a low bit rate coding method for speech and audio using a new analysis method named MLPC (Mel-LPC analysis). In the MLPC analysis method a spectrum envelope is estimated on a mel- or bark-frequency scale, so as to improve the spectral resolution in the low frequency band. This analysis is accomplished with about two-fold increase in computation over the standard LPC analysis. Our coding algorithm using the MLPC analysis consists of five key parts: time frequency transformation, inverse filtering by the MLPC spectrum envelope, power normalization, perceptual weighting estimation, and the multi-stage vector quantization. In subjective experiments, we have investigated the performance of MLPC analysis method, through the evaluation of paired comparison tests between the MLPC analysis and the standard LPC one in inverse filtering. In all bit rates, almost all the listeners feel decoding signals by the MLPC analysis method is superior to the LPC one. Especially in low bit rate, there is a great difference between them.

As a noise robust HMM, we previously proposed a frequency-weighted HMM (HMM-FW) whose covariance matrices are replaced by the inverse of frequency-weighting matrices. In this HMM, the frequency-weighting parameters were common to all classes and states, and were experimentally adjusted. In order to achieve further noise robustness, this paper examines the class- and state-dependent weighting parameters and their minimum error classification training (MCE) of their weighting characteristics. Using the NOISEX-92 database the MCE-trained HMM-FWs are shown to be more robust even under untrained noise conditions than both the previous HMM-FW and conventional HMM.

The Akebono (EXOS-D) satellite has been successfully observing the Earth's magnetosphere since it was launched on February 21, 1989. The objectives of VLF instruments on board the satellite were to investigate the behavior of plasma waves associated with accelerated auroral particles, wave-particle interaction mechanisms, and propagation characteristics of whistler-mode waves in the magnetosphere. The instruments measured not only the dynamic spectra of VLF waves up to 15 kHz by a wideband receiver, but also their absolute field intensities, wave normal vectors, and Poynting vectors. Two electric and three magnetic components with a bandwidth of about 50 Hz up to 12.5 kHz are sent to measure the wave normal vectors and Poynting vectors. The antenna impedance is measured to determine the correct absolute electric field intensities. The instruments have successfully measured the wave spectra, the wave normal vectors. Poynting vectors, the precise wave intensities, the antenna impedance, etc. The present paper describes the unique features of the instruments, especially the Poynting flux analyzers in more detail. Obtained scientific results are also reviewed.

This paper investigates the noise robustness of various LPC and filter bank based spectral matching measures with the aim of developing a reliable speech recognition system for noisy environments. In order to evaluate the recognition performance of the distance measures for noisy speech, speaker dependent isolated word recognition tests in white and colored noise environments were carried out using both clean and noisy reference spectra. The experimental results showed that their recognition performances are very different at large SNR mismatch conditions between test and reference, especially for clean reference, and that the low frequency weighted WLR is the most robust for white noise, and the log likelihood ratio is the worst for highly degraded input speech. Furthermore, pseudo peak weighted likelihood ratios based on filter bank spectra showed very high noise robust characteristics close to the corresponding LPC distance metrics.

This paper concerns a new approach to nonlinear spectral normalization to eliminate inter- speaker differences from frequency-band-limited speech. A frequency normalized distance between a test and a reference spectrum is defined on the basis of minimum mean square difference over all possible choices of frequency warping functions under certain constraints. This spectral distance is computed by means of dynamic programming after adaptively eliminating the individual glottal characteristics. Applications to the identification of steady-state vowels and the detection of words in connected speech is discussed.

This paper describes a method for text-independent speaker identification. In this method, in order to utilize phoneme-dependent personal information in addition to personal information common to all phonemes, multiple personal factor spaces are constructed by applying canonical discriminant analysis to the predetermined subspaces in the observation space. The decision is based on a liklihood measure derived from a posteriori probabilities in all the factor spaces. Using the 21-dimensional observation vectors obtained from every 40 msec voiced segments, the methods of construction of the subspaces and others were examined. An identification accuracy comparable to human listeners was achieved.

The feasibility of Monte Carlo linear extrapolation of multivariable functions is discussed. The method considered is a modified version of the Monte Carlo method for linear interpolation. An effective truncation procedure is introduced, which not only economizes the machine time to a greet extent, but also serves for the purpose of variance reduction. A few examples were tested, all of which have shown that the method is promising.