Tadipatri Uday Kiran Reddy

arXiv2023

Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Results

In this paper, we present a unified and general framework for analyzing the batch updating approach to nonlinear, high-dimensional optimization. The framework encompasses all the currently used batch updating approaches, and is applicable to nonconvex as well as convex functions. Moreover, the framework permits the use of noise-corrupted gradients, as well as first-order approximations to the gradient (sometimes referred to as "gradient-free" approaches). By viewing the analysis of the iterations as a problem in the convergence of stochastic processes, we are able to establish a very general theorem, which includes most known convergence results for zeroth-order and first-order methods. The analysis of "second-order" or momentum-based methods is not a part of this paper, and will be studied elsewhere. However, numerical experiments indicate that momentum-based methods can fail if the true gradient is replaced by its first-order approximation. This requires further theoretical analysis.

math.OC↗

arXiv2022

Evince the artifacts of Spoof Speech by blending Vocal Tract and Voice Source Features

With the rapid advancement in synthetic speech generation technologies, great interest in differentiating spoof speech from the natural speech is emerging in the research community. The identification of these synthetic signals is a difficult task not only for the cutting-edge classification models but also for humans themselves. To prevent potential adverse effects, it becomes crucial to detect spoof signals. From a forensics perspective, it is also important to predict the algorithm which generated them to identify the forger. This needs an understanding of the underlying attributes of spoof signals which serve as a signature for the synthesizer. This study emphasizes the segments of speech signals critical in identifying their authenticity by utilizing the Vocal Tract System(\textit{VTS}) and Voice Source(\textit{VS}) features. In this paper, we propose a system that detects spoof signals as well as identifies the corresponding speech-generating algorithm. We achieve 99.58\% in algorithm classification accuracy. From experiments, we found that a VS feature-based system gives more attention to the transition of phonemes, while, a VTS feature-based system gives more attention to stationary segments of speech signals. We perform model fusion techniques on the VS-based and VTS-based systems to exploit the complementary information to develop a robust classifier. Upon analyzing the confusion plots we found that WaveRNN is poorly classified depicting more naturalness. On the other hand, we identified that synthesizer like Waveform Concatenation, and Neural Source Filter is classified with the highest accuracy. Practical implications of this work can aid researchers from both forensics (leverage artifacts) and the speech communities (mitigate artifacts).

eess.AS↗