Search

Current induced spin-orbit effective magnetic fields in metal/ferromagnet/oxide trilayers provide a new way to manipulate the magnetization, which is an alternative to the conventional current induced spin transfer torque arising from noncollinear magnetization. Ta/CoFeB/MgO structures are expected to be useful for non-volatile memories and logic devices due to its perpendicular anisotropy and large current induced spin-orbit effective fields. However many aspects such as the angular and temperature dependent phenomena of the effective fields are little understood. Here, we evaluate the angular and temperature dependence of the current-induced spin-orbit effective fields considering contributions from both the anomalous and planar Hall effects. The longitudinal and transverse components of effective fields are found to have strong angular dependence on the magnetization direction at 300 K. The transverse field decreases significantly with decreasing temperature, whereas the longitudinal field shows weaker temperature dependence. Our results reveal important features and provide an opportunity for a more comprehensive understanding of current induced spin-orbit effective fields. [ABSTRACT FROM AUTHOR]

We theoretically investigate the threshold current for thermally activated switching of a perpendicular magnet by spin-orbit spin torque. Based on the Fokker-Planck equation, we obtain an analytic expression of the switching current, in agreement with numerical result. We find that thermal energy barrier exhibits a quasi-linear dependence on the current, resulting in an almost linear dependence of switching current on the log-scaled current pulse-width even below 10 ns. This is in stark contrast to standard spin torque switching, where thermal energy barrier has a quadratic dependence on the current and the switching current rapidly increases at short pulses. Our results will serve as a guideline to design and interpret switching experiments based on spinorbit spin torque. [ABSTRACT FROM AUTHOR]

It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test. [ABSTRACT FROM AUTHOR]

In the present study, a new stereophonic playback system was proposed, where the cross-talk signals would be reasonably cancelled at an arbitrary listener position. The system was composed of two major parts: the listener position tracking part and the sound rendering part. The position of the listener was estimated using acoustic signals from the listener (i.e. voice or hand-clapping signals). A direction of arrival (DOA) algorithm was adopted to estimate the directions of acoustic sources where the room reverberation effects were taken into consideration. A Cross-talk cancellation filter was designed using a free-field model. To determine the maximum tolerable shift of the listener position, a quantitative analysis of the channel separation ratio according to the displacement of the listener position was performed. Prototype hardware was implemented using a microprocessor board, a DSP board, a multi-channel ADC board and an analog frontend. The results showed that the average mean square error between the true direction of a listener and the estimated direction was about 5 degrees. More than 80% of the tested subjects indicated that better stereo images were obtained by the proposed system, compared with the non-processed signals. [ABSTRACT FROM AUTHOR]

Myoelectric signals (MESs) from the speaker's mouth region have been successfully shown to improve the noise robustness of automatic speech recognizers (ASRs), thus promising to extend their usability in implementing noise-robust ASR. In the recognition system presented herein, extracted audio and facial MES features were integrated by a decision fusion method, where the likelihood score of the audio-MES observation vector was given by a linear combination of class-conditional observation log-likelihoods of two classifiers, using appropriate weights. We developed a weighting process adaptive to SNRs. The main objective of the paper involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. These two parameters were determined by a method based on a maximum mutual information criterion. Acoustic and facial MES data were collected from five subjects, using a 60-word vocabulary. Four types of acoustic noise including babble, car, aircraft, and white noise were acoustically added to clean speech signals with SNR ranging from -14 to 31 dB. The classification accuracy of the audio ASR was as low as 25.5%. Whereas, the classification accuracy of the MES ASR was 85.2%. The classification accuracy could be further improved by employing the proposed audio-MES weighting method, which was as high as 89.4% in the case of babble noise. A similar result was also found for the other types of noise. [ABSTRACT FROM PUBLISHER]

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) frame- work. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model. [ABSTRACT FROM AUTHOR]

A voice transformation method which changes the source speaker's utterances so as to sound similar to those of a target speaker is described. Speaker individuality transformation is achieved by altering the LPC cepstrum, average pitch period and average speaking rate. The main objective of the work involves building a nonlinear relationship between the parameters for the acoustical features of two speakers, based on a probabilistic model. The conversion rules involve the probabilistic classification and a cross correlation probability between the acoustic features of the two speakers, The parameters of the conversion rules are estimated by estimating the maximum likelihood of the training data. To obtain transformed speech signals which are perceptually closer to the target speaker's voice, prosody modification is also involved. Prosody modification is achieved by scaling excitation spectrum and time scale modification with appropriate modification factors. An evaluation by objective tests and informal listening tests clearly indicated the effectiveness of the proposed transformation method. We also confirmed that the proposed method leads to smoothly evolving spectral contours over time, which, from a perceptual standpoint, produced results that were superior to conventional vector quantization (VQ)-based methods. [ABSTRACT FROM AUTHOR]

The automatic labeling of a large speech corpus plays an important role in the development of a high-quality Text-To-Speech (TTS) synthesis system. This paper describes a method for the automatic labeling of speech signals, which mainly involves the construction of a large database for a TTS synthesis system. The main objective of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Markov Model. A multilayer perceptron (MLP) was employed to refine the phone boundaries. To increase the accuracy of phoneme segmentation, several specialized MLPs were individually trained based on phonetic transition. The optimum partitioning of the entire phonetic transition space and the corresponding MLPs were constructed from the standpoint of minimizing the overall deviation from the hand-labeling position. The experimental results showed that more than 93% of all phone boundaries have a boundary deviation from a reference position smaller than 20 ms. We also confirmed that the database constructed using the proposed method produced results that were perceptually comparable to a hand-labeled database, based on subjective listening tests. [ABSTRACT FROM AUTHOR]

Deals with a study which proposed a context-adaptive smoothing method according to text information in text-to-speech synthesis. Approaches to solving discontinuities in the synthesized speech signal; Reason for the inconsistent performance of spectral smoothing; Features of context-adaptive smoothing.

This letter addresses a temporal decomposition (TD) technique that is based on a rate-distortion criterion. In the proposed TD scheme, a set of the interpolation functions is constructed from the given training corpus, and the optimum target points are found in the sense of minimizing, not only spectral distortion, but also bit rates. The results of the simulation show that an average spectral distortion of about 1.4 dB can be achieved at an average bit rate of about 8 bits/frame. [ABSTRACT FROM AUTHOR]