Speech recognition in noisy car environment based on OSALPC representation and robust similarity measuring techniques

The performance of the existing speech recognition systems degrades rapidly in the presence of background noise. The OSALPC (one-sided autocorrelation linear predictive coding) representation of the speech signal has shown to be attractive for speech recognition because of its simplicity and its high recognition performance with respect to the standard LPC in severe conditions of additive white noise. The aim of this paper is twofold: (1) to show that OSALPC also achieves good performance in a case of real noisy speech (in a car environment), and (2) to explore its combination with several robust similarity measuring techniques, showing that its performance improves by using cepstral liftering, dynamic features and multilabeling.Peer ReviewedPostprint (published version

5G無線通信における誤り訂正符号化方式の評価に関する研究

早大学位記番号:新8267早稲田大

Extended analysis of motion-compensated frame difference for block-based motion prediction error

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Fractal image compression and the self-affinity assumption : a stochastic signal modelling perspective

Bibliography: p. 208-225.Fractal image compression is a comparatively new technique which has gained considerable attention in the popular technical press, and more recently in the research literature. The most significant advantages claimed are high reconstruction quality at low coding rates, rapid decoding, and "resolution independence" in the sense that an encoded image may be decoded at a higher resolution than the original. While many of the claims published in the popular technical press are clearly extravagant, it appears from the rapidly growing body of published research that fractal image compression is capable of performance comparable with that of other techniques enjoying the benefit of a considerably more robust theoretical foundation. . So called because of the similarities between the form of image representation and a mechanism widely used in generating deterministic fractal images, fractal compression represents an image by the parameters of a set of affine transforms on image blocks under which the image is approximately invariant. Although the conditions imposed on these transforms may be shown to be sufficient to guarantee that an approximation of the original image can be reconstructed, there is no obvious theoretical reason to expect this to represent an efficient representation for image coding purposes. The usual analogy with vector quantisation, in which each image is considered to be represented in terms of code vectors extracted from the image itself is instructive, but transforms the fundamental problem into one of understanding why this construction results in an efficient codebook. The signal property required for such a codebook to be effective, termed "self-affinity", is poorly understood. A stochastic signal model based examination of this property is the primary contribution of this dissertation. The most significant findings (subject to some important restrictions} are that "self-affinity" is not a natural consequence of common statistical assumptions but requires particular conditions which are inadequately characterised by second order statistics, and that "natural" images are only marginally "self-affine", to the extent that fractal image compression is effective, but not more so than comparable standard vector quantisation techniques

Orthogonal transform feasibility study

The application of various orthogonal transformations to communication was investigated, with particular emphasis placed on speech and visual signal processing. The fundamentals of the one- and two-dimensional orthogonal transforms and their application to speech and visual signals are treated in detail

Transform coding of pictorial data

By using transform coding, image transmission rates as low as 0.5 bit/pel can be achieved. Generally, the bit rate reduction is achieved by allocating fewer bits to low energy high order coefficients, However, to ensure reasonably good picture quality, a large number of bits has to be allocated to high energy dc coefficients for both fine quantization and good channel error immunity, A technique has been developed that, in some cases, allows the de coefficients to be estimated at the receiver, thus eliminating a major source of difficulty with respect to channel errors. [Continues.