Development of a Two-Level Warping Algorithm and Its Application to Speech Signal Processing

In many different fields there are signals that need to be aligned or “warped” in order to measure the similarity between them. When two time signals are compared, or when a pattern is sought in a larger stream of data, it may be necessary to warp one of the signals in a nonlinear way by compressing or stretching it to fit the other. Simple point-to-point comparison may give inadequate results, because one part of the signal might be comparing different relative parts of the other signal/pattern. Such cases need some sort of alignment todo the comparison. Dynamic Time Warping (DTW) is a powerful and widely used technique of time series analysis which performs such nonlinear warping in temporal domain. The work in this dissertation develops in two directions. The first direction is to extend the this dynamic time warping to produce a two-level dynamic warping algorithm, with warping in both temporal and spectral domains. While there have been hundreds of research efforts in the last two decades that have applied and used the one-dimensional warping process idea between time series, extending DTW method to two or more dimensions poses a more involved problem. The two-dimensional dynamic warping algorithm developed here for a variety of speech signal processing is ideally suited. The second direction is focused on two speech signal applications. The First application is the evaluation of dysarthric speech. Dysarthria is a neurological motor speech disorder, which characterized by spectral and temporal degradation in speech production. Dysarthria management has focused primarily teaching patients to improve their ability to produce speech or strategies to compensate for their deficits. However, many individuals with dysarthria are not well-suited for traditional speaker-oriented intervention. Recent studies have shown that speech intelligibility can be improved by training the listener to better understand the degraded speech signal. A computer-based training tool was developed using a two-level dynamic warping algorithm to eventually be incorporated into a program that trains listeners to learn to imitate dysarthric speech by providing subjects with feedback about the accuracy of their imitation attempts during training. The second application is voice transformation. Voice transformation techniques aims to modify a subject’s voice characteristics to make them sound like someone else, for example from a male speaker to female speaker. The approach taken here avoids the need to find acoustic parameters as many voice transformation methods do, and instead deals directly with spectral information. Based on the two-Level DW it is straightforward to map the source speech to target speech when both are available. The resulted spectral warping signal produced as described above introduces significant processing artifacts. Phase reconstruction was applied to the transformed signal to improve the quality of the final sound. Neural networks are trained to perform the voice transformation

DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering the knowledge of various aspects related to events and different media

A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition

In this paper, we propose a novel family of windowing technique to compute Mel Frequency Cepstral Coefficient (MFCC) for automatic speaker recognition from speech. The proposed method is based on fundamental property of discrete time Fourier transform (DTFT) related to differentiation in frequency domain. Classical windowing scheme such as Hamming window is modified to obtain derivatives of discrete time Fourier transform coefficients. It has been mathematically shown that the slope and phase of power spectrum are inherently incorporated in newly computed cepstrum. Speaker recognition systems based on our proposed family of window functions are shown to attain substantial and consistent performance improvement over baseline single tapered Hamming window as well as recently proposed multitaper windowing technique

On RG-flow and the Cosmological Constant

The AdS/CFT correspondence implies that the effective action of certain strongly coupled large $N$ gauge theories satisfy the Hamilton-Jacobi equation of 5d gravity. Using an analogy with the relativistic point particle, I construct a low energy effective action that includes the Einstein action and obeys a Callan-Symanzik-type RG-flow equation. It follows from the flow equation that under quite general conditions the Einstein equations admit a flat space-time solution, but other solutions with non-zero cosmological constant are also allowed. I discuss the geometric interpretation of this result in the context of warped compactifications.Comment: 11 pages, 1 figure, contribution to the proceedings of Strings '99, misprint correcte

Scales and hierarchies in warped compactifications and brane worlds

Warped compactifications with branes provide a new approach to the hierarchy problem and generate a diversity of four-dimensional thresholds. We investigate the relationships between these scales, which fall into two classes. Geometrical scales, such as thresholds for Kaluza-Klein, excited string, and black hole production, are generically determined soley by the spacetime geometry. Dynamical scales, notably the scale of supersymmetry breaking and moduli masses, depend on other details of the model. We illustrate these relationships in a class of solutions of type IIB string theory with imaginary self-dual fluxes. After identifying the geometrical scales and the resulting hierarchy, we determine the gravitino and moduli masses through explicit dimensional reduction, and estimate their value to be near the four-dimensional Planck scale. In the process we obtain expressions for the superpotential and Kahler potential, including the effects of warping. We identify matter living on certain branes to be effectively sequestered from the supersymmetry breaking fluxes: specifically, such "visible sector" fields receive no tree-level masses from the supersymmetry breaking. However, loop corrections are expected to generate masses, at the phenomenologically viable TeV scale.Comment: 33 pages, LaTeX. v2: reference added v3: reference added, typos correcte

Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

Mandarin Chinese is characterized by being a tonal language; the pitch (or $F_0$) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase which must be accounted for in any analysis which attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase and duration is presented which combines elements from Functional Data Analysis, Compositional Data Analysis and Linear Mixed Effects Models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and non-linguistic covariates. The model is applied to the COSPRO-1 data set, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50 thousand phonetically diverse sample $F_0$ contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation.Comment: 49 pages, 13 figures, small changes to discussio