A minimax search algorithm for robust continuous speech recognition

In this paper, we propose a novel implementation of a minimax decision rule for continuous density hidden Markov-model-based robust speech recognition. By combining the idea of the minimax decision rule with a normal Viterbi search, we derive a recursive minimax search algorithm, where the minimax decision rule is repetitively applied to determine the partial paths during the search procedure. Because of the intrinsic nature of a recursive search, the proposed method can be easily extended to perform continuous speech recognition. Experimental results on Japanese isolated digits and TIDIGITS, where the mismatch between training and testing conditions is caused by additive white Gaussian noise, show the viability and efficiency of the proposed minimax search algorithm.published_or_final_versio

Improving Viterbi Bayesian predictive classification via sequentialBayesian learning in robust speech recognition

We extend our previously proposed Viterbi Bayesian predictive classification (VBPC) algorithm to accommodate a new class of prior probability density function (PDF) for continuous density hidden Markov model (CDHMM) based robust speech recognition. The initial prior PDF of CDHMM is assumed to be a finite mixture of natural conjugate prior PDF's of its complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite mixture PDF's which retain the required most significant terms in the true posterior density according to their contribution to the corresponding predictive density. Then the updated mixture PDF is used to improve the VBPC performance. The experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed technique.published_or_final_versio

Robust speech recognition based on a Bayesian prediction approach

We study a category of robust speech recognition problem in which mismatches exist between training and testing conditions, and no accurate knowledge of the mismatch mechanism is available. The only available information is the test data along with a set of pretrained Gaussian mixture continuous density hidden Markov models (CDHMMs). We investigate the problem from the viewpoint of Bayesian prediction. A simple prior distribution, namely constrained uniform distribution, is adopted to characterize the uncertainty of the mean vectors of the CDHMMs. Two methods, namely a model compensation technique based on Bayesian predictive density and a robust decision strategy called Viterbi Bayesian predictive classification are studied. The proposed methods are compared with the conventional Viterbi decoding algorithm in speaker-independent recognition experiments on isolated digits and TI connected digit strings (TIDTGITS), where the mismatches between training and testing conditions are caused by: (1) additive Gaussian white noise, (2) each of 25 types of actual additive ambient noises, and (3) gender difference. The experimental results show that the adopted prior distribution and the proposed techniques help to improve the performance robustness under the examined mismatch conditions.published_or_final_versio

Sequential Bayesian learning of CDHMM based on finite mixture approximation of its prior/posterior density

Proposes a sequential Bayesian learning strategy of a continuous-density hidden Markov model (CDHMM) based on a finite mixture approximation of its prior/posterior density. The initial prior density of the CDHMM is assumed to be a finite mixture of natural conjugate prior probability density functions (PDFs) of the complete-data density. With the new observation data, the true posterior PDF is approximated by the same type of finite-mixture PDFs which retain the required most significant terms in the true posterior density according to their contribution to the corresponding Bayesian predictive density by using an N-best beam search algorithm. Then, the updated mixture PDF is used in the VBPC (Viterbi Bayesian predictive classification) method to deal with unknown mismatches in robust speech recognition. Experimental results on a speaker-independent recognition task of isolated Japanese digits confirm the viability and the usefulness of the proposed method.published_or_final_versio

Hawking Radiation from Non-Extremal D1-D5 Black Hole via Anomalies

We take the method of anomaly cancellation for the derivation of Hawking radiation initiated by Robinson and Wilczek, and apply it to the non-extremal five-dimensional D1-D5 black hole in string theory. The fluxes of the electric charge flow and the energy-momentum tensor from the black hole are obtained. They are shown to match exactly with those of the two-dimensional black body radiation at the Hawking temperature.Comment: 14 page

Thermomechanical Characterization And Modeling For TSV Structures

Continual scaling of devices and on-chip wiring has brought significant challenges for materials and processes beyond the 32-nm technology node in microelectronics. Recently, three-dimensional (3-D) integration with through-silicon vias (TSVs) has emerged as an effective solution to meet the future technology requirements. Among others, thermo-mechanical reliability is a key concern for the development of TSV structures used in die stacking as 3-D interconnects. This paper presents experimental measurements of the thermal stresses in TSV structures and analyses of interfacial reliability. The micro-Raman measurements were made to characterize the local distribution of the near-surface stresses in Si around TSVs. On the other hand, the precision wafer curvature technique was employed to measure the average stress and deformation in the TSV structures subject to thermal cycling. To understand the elastic and plastic behavior of TSVs, the microstructural evolution of the Cu vias was analyzed using focused ion beam (FIB) and electron backscattering diffraction (EBSD) techniques. Furthermore, the impact of thermal stresses on interfacial reliability of TSV structures was investigated by a shear-lag cohesive zone model that predicts the critical temperatures and critical via diameters.Microelectronics Research Cente

Fabrication and characterizations of proton-exchanged LiNbO3 waveguides fabricated by inductively coupled plasma technique

This Letter reports the use of an inductively coupled plasma technique for fabrication of proton-exchanged (PE) LiNbO3 (LN) waveguides. Planar and stripe waveguides have been formed in Y-cut LN which are difficult to obtain with the conventional molten acid method due to the occurrence of surface damage. Secondary ion mass spectrometry, scanning electron microscopy, and infrared absorption spectrum characterization results revealed that a uniform vertical PE profile with a single low order crystal phase has been directly obtained as a result of this unique process. X-ray photoelectron spectroscopy characterization of the treated surface revealed the existence of NbO as the cause for a sometimes darkened surface and confirms the ability to completely restore the surface to LN by oxygen plasma treatment. Atomic force microscopy measurement confirms that good surface quality has been maintained after regeneration of the surface to LN