Abstract

The problem of unconditionally reliable message transmission (URMT) is to design a protocol which when run by players in a network enables a sender s to deliver a message to a receiver r with high probability, even when some players in the network are under the control of an unbounded adversary. Renault and Tomala [JoC2008] gave a characterization of undirected neighbour networks over which URMT tolerating Byzantine adversary is possible. In this paper, we generalize their result to the case of directed networks.

Abstract

The VLSI layout designing is a highly complex process and hence a layout is often subjected to Layout Verification that includes (a) Design Rule Checking to check if the layout satisfies various design rules and (b) Connectivity Extraction to check if the components of the layout are properly electrically connected. In this work we study two geometric query problems which have applications in the above layout verification phase.

Abstract

We study the problem of information-theoretically secure message transmission (SMT) in asynchronous directed networks. In line with the literature, the distrust and failures of the network is captured via a computationally unbounded Byzantine adversary that may corrupt some subset of nodes. We give a characterization of networks over which SMT from sender S to receiver R is possible in both the well-known settings, namely perfect SMT (PSMT) and unconditional SMT (USMT). We distinguish between two variants of USMT: one in which R can output an incorrect message (with small probability) and another in which R never outputs a wrong message, but may choose to abort (with small probability). We also provide efficient protocols for an important class of networks.

Abstract

For voiced speech, the most significant excitation takes place around the instant of glottal closure. Glottal closure in-stants (GCI) information is useful for accurate speech analysis.In particular accurate spectrum analysis is performed by con-sidering the speech in the intervals of glottal closure. In this pa-per we propose an approach for detection of GCI by exploring Bessel feature, and the use of AM-FM signal. Using appropri-ate range of Bessel coefficients, the narrow band, band limited signal is obtained for the given signal.The band limited signal isconsidered as AM-FM signal. The signal is band limited for 0-300 Hz to remove effect of formants. Amplitude envelope (AE)function of the AM-FM signal model has been estimated by the discrete energy separation algorithm (DESA). The performance of the method is demonstrated using CMU-Arctic database. The corresponding electro-glottograph (EGG) signals are used as a reference for the validation of the detected GCI locations.

Abstract

Face recognition is one of the most challenging tasks in the field of image processing. This paper presents an optimized edge detection algorithm for the task of face recognition. In this method a gradient based filter using a wide convolution kernel is applied on the image to extract the edges. Later a thinning algorithm optimized for the wide convolution kernel is applied on the extracted edges. The advantages of this method over other gradient based methods is its ability to find the missing edges more successfully and boost the significant edges of the overall facial contour.

Abstract

Human-computer interaction plays a significant role for literate/illiterate and visually challenged users to access information. The mode of human-computer interaction could be speech, text, gestures, facial expressions, symbols etc., or a combination of these. An interaction could be in the form of a conversation including statements, questions, answers and expressions. A (uni-modal or multi-modal) conversational type of human-computer interaction is often referred to as a conversational system. A conversational system with speech as an input mode assumes significance as speech is a natural means of communication for human beings. The goal of a speech-based conversation (SBC) system is to provide information by conversing with a human-being in a natural fashion. Our objective is to develop speech based conversational systems for in formation access in an Indian language.

Abstract

Glottal closure instants (GCI) information is useful for accurate speech analysis. In particular accurate spectrum analysis is performed by considering the speech in the intervals of glottal closure. In this paper we propose an approach for detection of GCI based on Bessel features, amplitude and frequency modulated (AM-FM) signal. Using appropriate range of Bessel coefficients, the narrow band, band limited signal is obtained for the given signal. The band limited signal is considered as a AM-FM signal. The signal is band limited for0-300 Hz to remove effect of formants. Amplitude Envelope (AE)function of the AM-FM signal model has been estimated by the discrete energy separation algorithm (DESA). We experimentally evaluated our approach to detect GCI on CMU-Arctic database.The corresponding electro-glottograph (EGG) signals are used as a reference for the validation of the detected GCI locations.

Abstract

Voice onset time is an important temporal feature which is often overlooked in speech perception, speech recognition as well as accent detection. The VOT in unvoiced stops varies with a number of factors, among which the most established one is the place of articulation. In this paper we propose an approach for the automatic detection of VOT. The proposed method uses Bessel expansion to emphasize the vowel and consonant regions of stop consonant vowel units (SCV) such as /ka/, /Ta/, /ta/ and /pa/. AM-FM signal has been emphasized after appropriate consideration of the range of Bessel coefficients,separately for the vowel and consonant regions of SCV units. There constructed signal from the Bessel expansion is a narrow-band AM-FM signal, therefore the amplitude envelope (AE) function for the emphasized signal can be estimated using discrete energy separation algorithm (DESA). For the detection of VOT, both the AE of vowel and consonat emphasized signal has been analyzed.Detection of VOT is analyzed for the continuous speech corpus consisting of recording television broadcast news bulletins.

Abstract

In this paper, we explore Bessel features to determine the number of speakers from multispeaker speech signals collected simultaneously from a pair of spatially separated microphones. The arrival of the speech signals from speaker to microphones gives the time delays of given speaker. The time delays can be estimated by performing the cross-correlation to the band limited multispeaker signals collected at the two microphones. Signals are band limited using a finite number of Bessel coefficients. The computer simulation results demonstrate the proposed method is efficient compared to existing methods.

Abstract

The Independent Component Analysis with Reference (ICA - R) also called as constrained ICA (cICA) extracts only the desired source signals from the mixture of source signals by incorporating some prior information into the separation process. To overcome the problem of designing the reference signal when there is no prior information about the desired signal in the cICA, an improved method is proposed to use a different speech signal generated by the same physical source. The cICA is extended to use Bessel coefficients of the observed signals and the reference signal for processing as they converge faster than the other transformations. Since the Bessel functions provide the desired properties, efficient in representing speech signals, less memory storage they have been exploited in speech processing [1]. The results demonstrate the efficiency of the proposed method