34060618
A Minimum Boundary Error Framework for Automatic Phonetic Segmentation
This paper presents a novel framework for HMM-based automatic phonetic segmentation that improves the accuracy of placing phone boundaries. In the framework, both training and segmentation approaches are proposed according to the minimum boundary error (MBE) criterion, which tries to minimize the expected boundary errors over a set of possible phonetic alignments. This framework is inspired by the recently proposed minimum phone error (MPE) training approach and the minimum Bayes risk decoding algorithm for automatic speech recognition. To evaluate the proposed MBE framework, we conduct automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. MBE segmentation with MBE-trained models can identify 80.53% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.10% identified by conventional ML segmentation with ML-trained models. Moreover, by using the MBE framework, only 7.15% of automatically labeled phone boundaries have errors larger than 20 ms.
7308728
Fluent speech prosody: Framework and modeling
Abstract The prosody of fluent connected speech is much more complicated than concatenating individual sentence intonations into strings. We analyzed speech corpora of read Mandarin Chinese discourses from a top–down perspective on perceived units and boundaries, and consistently identified speech paragraphs of multiple phrases that reflected discourse rather than sentence effects in fluent speech. Subsequent cross-speaker and cross-speaking-rate acoustic analyses of identified speech paragraphs revealed systematic cross-phrase prosodic patterns in every acoustic parameter, namely, F0 contours, duration adjustment, intensity patterns, and in addition, boundary breaks. We therefore argue for a higher prosodic node that governs, constrains, and groups phrases to derive speech paragraphs. A hierarchical multi-phrase framework is constructed to account for the governing effect, with complimentary production and perceptual evidences. We show how cross-phrase F0 and syllable duration patterns templates are derived to account for the tune and rhythm characteristic to fluent speech prosody, and argue for a prosody framework that specifies phrasal intonations as subjacent sister constituent subject to higher terms. Output fluent speech prosody is thus cumulative results of contributions from every prosodic layer. To test our framework, we further construct a modular prosody model of multiple-phrase grouping with four corresponding acoustic modules and begin testing the model with speech synthesis. To conclude, we argue that any prosody framework of fluent speech should include prosodic contributions above individual sentences in production, with considerations of its perceptual effects to on-line processing; and development of unlimited TTS could benefit most appreciably by capturing and including cross-phrase relationships in prosody modeling.
16950996
Joint source-channel coding for wireless object-based video communications utilizing data hiding
In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection
17389325
Analysis of gray level corner detection
Abstract In this paper the analysis of gray level corner detection has been carried out. Performances of various cornerness measures are discussed with respect to four performances of robustness: detection, localization, stability and complexity. We have analyzed the interior differential features of the image surface of these cornerness measures. This paper presents a new approach called gradient-direction corner detector for the corner detection which is developed from the popular Plessey corner detection. The gradient-direction corner detector is based on the measure of the gradient module of the image gradient direction and the constraints of the false corner response suppression.
37744397
Stay Current and Relevant in Data Mining Research
In a recent editorial of the Bioinformatics journal, Dr. Pavel Pevzner, a pioneering researcher in the field of bioinformatics, made the following statement [1]: For many years algorithms were taught exclusively to computer scientists, with relatively few students from other disciplines attending algorithms courses. A biology student in an algorithms class would be a surprising and unlikely (though not entirely unwelcome) guest in the 1990s. Things change; some biology students now take some sort of Algorithms 101. At the same time, curious computer science students often take Genetics 101.
14076500
False-Peaks-Avoiding Mean Shift Method for Unsupervised Peak-Valley Sliding Image Segmentation
The mean shift (MS) algorithm is sensitive to local peaks. In this paper, we show both empirically and analytically that when using sample data, the reconstructed PDF may have false peaks. We show how the occurrence of the false peaks is related to the bandwidth h of the kernel density estimator, using examples of gray-level image segmentation. It is well known that in MS-based approaches, the choice of h is important: we provide a quantitative relationship between false peaks and h. For the gray-level image segmentation problem, we provide a complete unsupervised peak-valley sliding algorithm for gray- level image segmentation.
14473430
An optimal shape encoding scheme using skeleton decomposition
This paper presents an operational rate-distortion (ORD) optimal approach for skeleton-based boundary encoding. The boundary information is first decomposed into skeleton and distance signals, by which a more efficient representation of the original boundary results. Curves of arbitrary order are utilized for approximating the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals and for all boundaries within a frame, so that the overall distortion is minimized. The problem is solved with the use of Lagrangian relaxation and a shortest path algorithm in a 4D directed acyclic graph (DAG) we propose. By defining a path selection pattern, we reduce the computational complexity of the 4D DAG shortest path algorithm from O(N/sup -5/) to O(N/sup -4/), where N is the number of admissible control points for a skeleton. A suboptimal solution is also presented for further reducing the computational complexity of the algorithm to O(N/sup -2/). The proposed algorithm outperforms experimentally other competing algorithms.