waashk/dblp huggingface dataset:
modelling the prepausal lengthening effect for speech recognition: a dynamic bayesian network approach. speech has a property that the speech unit preceding a speech pause tends to lengthen. this work presents the use of a dynamic bayesian network to model the prepausal lengthening effect for robust speech recognition. specifically, we introduce two distributions to model inter-state transitions in prepausal and non-prepausal words, respectively. the selection of the transition distributions depends on a random variable whose value is influenced by whether a pause will appear between the current and the following word. two experiments are presented here. the first one considers pauses hypothesised during speech decoding. the second one employs an extra component for speech/non-speech determination. by modelling the prepausal lengthening effect we achieve a 5.5% relative reduction in word error rate on the 500-word task of the svitchboard corpus.
a bayesian networks approach for dialog modeling: the fusion bn. bayesian networks, bns, are suitable for mixed-initiative dialog modeling allowing a more flexible and natural spoken interaction. this solution can be applied to identify the intention of the user considering the concepts extracted from the last utterance and the dialog context. subsequently, in order to make a correct decision regarding how the dialog should continue, unnecessary, missing, wrong, optional and required concepts have to be detected according to the inferred goals. this information is useful to properly drive the dialog prompting for missing concepts, clarifying for wrong concepts, ignoring unnecessary concepts and retrieving those required and optional. this paper presents a novel bns approach where a single bn is obtained from n goal-specific bns through a fusion process. the new fusion bn enables a single concept analysis which is more consistent with the whole dialog context.
time-varying opportunistic protocol for maximizing sensor networks lifetime. we consider transmission scheduling by medium access control (mac) protocols for energy limited wireless sensor networks (wsn) in order to maximize the network lifetime. time-varying opportunistic protocol (top) for maximizing the network lifetime is proposed. by executing top each sensor exploits local channel state information (csi) and local residual energy information (rei). top implements opportunistic strategy in terms of favoring sensors with better channels when the network is young, while less opportunistic and more conservative strategy in terms of prioritizing sensors with higher residual energy when the network is old. top significantly simplifies the implementation of carrier sensing as compared to other distributed mac protocols. simulation results show that top achieves significant performance gains over other distributed mac protocols.
segmentation of malaria parasites in peripheral blood smear images. detection of malaria parasites in stained blood smears is critical for treatment of the disease. automation of this process will help in reducing the time taken for diagnosis and the chance for human errors. however, the variability and artifacts in microscope images of blood samples pose significant challenges for accurate detection. a scheme based on hsv color space that segments red blood cells and parasites by detecting dominant hue range and by calculating optimal saturation thresholds is presented in this paper. methods that are less computation-intensive than existing approaches are proposed to remove artifacts. the scheme is evaluated using images taken from leishman-stained blood smears. sensitivity and specificity of the scheme are found to be 83% and 98% respectively.
ensemble speaker and speaking environment modeling approach with advanced online estimation process. recently, we proposed an ensemble speaker and speaking environment modeling (essem) framework to characterize speaker variability and speaking environments. in contrast to multi-style training, essem uses single-style training to prepare multiple sets of environment-specific acoustic models. the ensemble of these acoustic models forms a prior structure of the environment for flexible prediction of unknown environment during testing. in this study, we present methods to further improve the precision for model characterization. we first study a weighted n-best information technique to well utilize the n-best transcription hypothesis in an unsupervised adaptation manner. next, we introduce cohort selection and environment space adaptation techniques to online improve the resolution and coverage of the prior structure. with an integration of the proposed methods, we further improve the essem performance over our previous study. on the aurora-2 task, essem achieves an average word error rate (wer) of 4.64%, corresponding to a 15.64% relative wer reduction over our best baseline result (5.50% to 4.64% wer) obtained with multi-condition training.
empirical error rate minimization based linear discriminant analysis. linear discriminant analysis (lda) is designed to seek a linear transformation that projects a data set into a lower-dimensional feature space while retaining geometrical class separability. however, lda cannot always guarantee better classification accuracy. one of the possible reasons lies in that its formulation is not directly associated with the classification error rate, so that it is not necessarily suited for the allocation rule governed by a given classifier, such as that employed in automatic speech recognition (asr). in this paper, we extend the classical lda by leveraging the relationship between the empirical classification error rate and the mahalanobis distance for each respective class pair, and modify the original between-class scatter from a measure of the squared euclidean distance to the pairwise empirical classification accuracy for each class pair, while preserving the lightweight solvability and taking no distributional assumption, just as what lda does. experimental results seem to demonstrate that our approach yields moderate improvements over lda on the large vocabulary continuous speech recognition (lvcsr) task.
robust nlos multipath mitigation for toa estimation. a technique that consists of correlation followed by thresholding is widely used to mitigate multipath effects, especially in the presence of non-line-of-sight (nlos) effects, for toa estimation. in practice, an accurate threshold that the technique relies on is difficult to obtain, since optimizing the threshold requires prior knowledge of channel statistics and signal/noise power, which are not always known a priori nor are easy to estimate. in this paper, we propose a new thresholding method that does not rely on such prior knowledge and is robust against an inaccurate threshold.
lattice-based mllr for speaker recognition. maximum-likelihod linear regression (mllr) transform coefficients have shown to be useful features for text-independent speaker recognition systems. these use mllr coefficients computed on a large vocabulary continuous speech recognition system (lvcsr) as features and support vector machines(svm) classification. however, performance is limited by transcripts, which are often erroneous with high word error rates (wer) for spontaneous telephone speech applications. in this paper, we propose using lattice-based mllr to overcome this issue. using wordlattices instead of 1-best hypotheses, more hypotheses can be considered for mllr estimation and, thus, better models are more likely to be used. as opposed to standard mllr, language model probabilities are taken into account as well. we show how systems using lattice mllr outperform standard mllr systems in the speaker recognition evaluation (sre) 2006. comparison to other standard acoustic systems is provided as well.
recognizing coordinated multi-object activities using a dynamic event ensemble model. while video-based activity analysis and recognition has received broad attention, existing body of work mostly deals with single object/person case. modeling involving multiple objects and recognition of coordinated group activities, present in a variety of applications such as surveillance, sports, biological records, and so on, is the main focus of this paper. unlike earlier attempts which model the complex spatial temporal constraints among different activities of multiple objects with a parametric bayesian network, we propose a dynamic ‘event ensemble’ framework as a data-driven strategy to characterize the group motion pattern without employing any specific domain knowledge. in particular, we exploit the riemannian geometric property of the set of ensemble description functions and develop a compact representation for group activities on the ensemble manifold. an appropriate classifier on the manifold is then designed for recognizing new activities. experiments on football play recognition demonstrate the effectiveness of the framework.
realizable equalizers for frequency selective mimo channels with cochannel interference. we consider realizable linear and decision feedback equalization (dfe) of frequency selective multiple-input multiple-output (mimo) channels in the presence of cochannel interference (cci). equalizers that are optimal in the minimum mean square error (mmse) sense are derived with and without zero forcing (zf) constraint. it is shown that all problems can be reduced to h2 optimal deconvolution, for which a novel algorithm is presented.
a detection-based approach to broadcast news video story segmentation. a detection-based paradigm decomposes a complex system into small pieces, solves each subproblem one by one, and combines the collected evidence to obtain a final solution. in this study of video story segmentation, a set of key events are first detected from heterogeneous multimedia signal sources, including a large scale concept ontology for images, text generated from automatic speech recognition systems, features extracted from audio track, and high-level video transcriptions. then a discriminative evidence fusion scheme is investigated. we use the maximum figure-of-merit learning approach to directly optimize the performance metrics used in system evaluation, such as precision, recall, and f1 measure. some experimental evaluations conducted on the trecvid 2003 dataset demonstrate the effectiveness of the proposed detection-based paradigm. the proposed framework facilitates flexible combination and extensions of event detector design and evidence fusion to enable other related video applications.
covariate shift adaptation for semi-supervised speaker identification. in this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of non-stationarity such as session dependent variation, the recording environment change, and physical condition/emotion. we assume that the utterance variation follows the covariate shift model, where only the utterance sample distribution changes in the training and test phases. our method consists of weighted versions of kernel logistic regression and cross-validation and is theoretically shown to have the capability of alleviating the influence of covariate shift. we experimentally show through text-independent speaker identification simulations that the proposed method is promising in dealing with variations in session dependent utterance variation.
speech enhancement based on joint time-frequency segmentation. we present an algorithm to decompose speech into transient and non-transient components. our algorithm, the joint timefrequency segmentation algorithm, uses the wavelet packet coefficients of the speech signal and represents them as tiles of a time-frequency representation adapted to the characteristics of the signal itself. any wavelet packet coefficient, whose tiling height is larger than or equal to the tiling width is characterized as a transient coefficient and vice versa for the nontransient coefficient. the transient component is selectively amplified and recombined with the original speech to generate the modified speech with energy adjusted to be equal to the energy of the original speech. the psychoacoustic tests performed with fourteen human listeners show that the speech modification significantly improves speech intelligibility in background noise, i.e., for 10% absolute at 0db to 31% absolute at −30db.
the use of acoustically detected filled and silent pauses in spontaneous speech recognition. in recognizing spontaneous speech, the performance of typical speech recognizers tends to be degraded by filled and silent pauses, which are hesitation phenomena frequently occurred in such speech. in this paper, we present a method for improving the performance of a speech recognizer by detecting and handling both filled pauses (lengthened vowels) and silent (unfilled) pauses. our method automatically detects these pauses by using a bottom-up acoustical analysis in parallel with a typical speech decoding process, and then incorporates the detected results into the decoding process. from the results of experiments conducted using the ciair spontaneous speech corpus, the effectiveness of the proposed method was confirmed.
bispectrum on finite groups. the algebraic theory of finite groups appears in signal processing problems involving the statistical analysis of ranked data and the construction of invariants for pattern recognition. standard signal processing techniques involving spectral analysis are, in theory, possible for data defined on finite groups by using the fourier transform provided by group representations. however, one such technique, the bispectrum, which is useful for analysing non-gaussian data as well as for constructing geometric invariants, has not been explored in detail for finite groups. this paper shows how to construct the bispectrum on an arbitrary finite group or homogeneous space and explores its properties. examples are given using the symmetric group as well as wreath-product groups. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
efficient speech indexing and search for embedded devices using uniterms. in this paper, we present an efficient method of speech indexing and search using phoneme sequences called uniterms. in the indexing stage, a collection of uniterms and uniterm sequences is extracted from the target speech database by applying statistical scoring to each data item's phoneme lattice. in the search stage, each speech query's phoneme lattice is used to select candidate uniterms from the collection. these uniterms are applied in a speech recognition engine to convert the speech query into a uniterm lattice, from which we obtain a set of candidate uniterm sequences, each of which can be mapped to a search result item. not only is this method a significant improvement over previous phoneme-based methods, it is shown that explicit sequential comparison of uniterms in query and target data can be avoided using the proposed method without loss of search performance. avoiding sequential comparison allows better handling of transposition of words, and for the case where queries have word orders different from their intended targets, the proposed method can potentially bring about significant improvement.
mimo multi-channel beamforming in double-scattering channels. in this paper, we investigate the performance of multiple-input multiple-output (mimo) multi-channel beamforming (mb) systems in double-scattering channels. in particular, we first derive new expressions for the marginal ordered eigen-value distributions of the double-scattering channel matrices. based on these results, we present analytical expressions for the symbol error rate of mimo mb. it is demonstrated that mb sub-channels can achieve full spatial diversity even in the presence of double scattering, if the number of effective scatterers is greater than or equal to the maximum number of transmit and receive antennas.
maximum likelihood principle for dna copy number analysis. microarray technologies had been used to measure dna copy number data. the copy number represents the relative fluorescent intensity level between control and test dna samples. variation of this number may lead to many genetic diseases such as cancer. unfortunately, the observed copy numbers are corrupted by noise due to experimental errors and probes accuracy, making the variations hard to detect. different techniques had been proposed to denoise the data and to extract the important feature such as the breakpoints from the variant regions. in this paper, we present a robust procedure for the analysis of dna copy number data based on maximum likelihood principle using global information of the entire data record. we show that dynamic programming can be used to compute the dna copy number estimates and reduce the computational complexity. furthermore, we employ the minimum description length rule to estimate the number of unknown parameters. using simulated and real data, we show that the proposed method outperforms other popular commercial software and published algorithms.
low delay moving-horizon multiple-description audio coding forwireless hearing aids. in this work, we construct a novel scheme for efficient perceptual coding of audio for robust communication between encoders and wireless hearing aids. to limit the physical size of the hearing aids and to reduce power consumption and thereby increase the lifetime expectancy of the batteries, the hearing aids are constrained to be of low complexity. we therefore provide an asymmetric strategy where most of the computational load is placed at the encoding side. we make use of multiple-description coding. this combats possible erasures on the wireless link between the encoder and the hearing aids without introducing significant delay. furthermore, we employ psychoacoustically optimized noise-shaping quantizers based on the moving-horizon principle, which exploits a finite prediction horizon.
full diversity under multiple carrier frequency offsets of a family of space-frequency codes. a cooperative system may have both timing errors and multiple carrier frequency offsets (cfos). to combat timing errors, space-frequency (sf) coded ofdm systems have been recently proposed for cooperative communications to achieve both full cooperative and full multipath diversities without time synchronization requirement. in this paper, we study the effect of multiple cfos from relay nodes on a family of rotation based sf codes. we find that they can still achieve full diversities under the condition that the absolute values of normalized cfos are less than 0.5. we further show that this full diversity property still holds for a complexity-reduced two-stage zero forcing (zf) aided maximum likelihood (ml) decoding method, for which a zf method is used to equalize multiple cfos before ml decoding.
data hiding in hard-copy text documents robust to print, scan and photocopy operations. this paper describes a method for hiding data inside printed text documents that is resilient to print/scan and photocopying operations. using the principle of channel coding with side information, the embedder inserts a message into a text document while treating the content of the document as known interference. the data is embedded by making small changes to text characters before the document is printed. using a simple correlation-based detector in conjunction with an error correction code, the hidden data can be extracted from a photocopy of the printed document. by enhancing the detector with an optical character recognition algorithm, the embedded data can be extracted even after multiple rounds of photocopying. results from subjective tests show that the changes made by the embedding algorithm, while perceptible, are not obtrusive to a lay reader.
efficient minimum-phase prefilter computation using fast ql-factorization. this paper presents a novel approach for computing both the minimum-phase filter and the associated all-pass filter in a computationally efficient way using fast ql-factorization. a desirable property of this approach is that the complexity is independent of the size of the matrix being ql-factorized. instead, the complexity scales with the required precision of the filters as well as the filter length.
vlsi implementation of an effective lattice reduction algorithm with fixed-point considerations. lattice reduction-aided equalization techniques have emerged as a low-complexity method to achieve the same diversity as maximum likelihood detectors. we address the vlsi implementation of these lr-aided equalizers by modifying the clll algorithm from a fixed-point hardware perspective. we then apply the modified algorithm together with additional micro-architecture and operation scheduling enhancements to create an updated clll processor. finally, through ber simulations and fpga synthesis results we demonstrate the suitability of our clll processor for integration into a 64-qam mimo system.
quickest change detection in multiple on-off processes. a bayesian formulation of quickest change detection in multiple on-off processes is obtained within a decision-theoretic framework. for geometrically distributed busy and idle times, we show that the optimal joint design of channel switching and change detection has a simple threshold structure under a mild condition. extensions to arbitrarily distributed busy and idle times, in particular, heavy tail distributions, are discussed. we show that this problem presents a fresh twist to the classic problem of quickest change detection that considers only one stochastic process. we demonstrate that the key to quickest change detection in multiple processes is to abandon the current process when its state is unlikely to change in the near future (as indicated by the measurements obtained so far) and seek opportunities in a new process to avoid realizations of long busy periods. this problem arises in spectrum opportunity detection in cognitive radio networks where a secondary user searches for idle channels in the spectrum.
chirp rate estimation of speech based on a time-varying quasi-harmonic model. the speech signal is usually considered as stationary during short analysis time intervals. though this assumption may be sufficient in some applications, it is not valid for high-resolution speech analysis and in applications such as speech transformation and objective voice function assessment for detection of voice disorders. in speech, there are non stationary components, for instance time-varying amplitudes and frequencies, which may change quickly over short time intervals. in this paper, a previously suggested time-varying quasi-harmonic model is extended in order or to estimate the chirp rate for each sinusoidal component, thus successfully tracking fast variations in frequency and amplitude. the parameters of the model are estimated through linear least squares and the model accuracy is evaluated on synthetic chirp signals. experiments on speech signals indicate that the new model is able to efficiently estimate the signal component chirp rates, providing means to develop more accurate speech models for high-quality speech transformations.
image deconvolution using a gaussian scale mixtures model to approximate the wavelet sparseness constraint. this paper proposes to use an extended gaussian scale mixtures (gsm) model instead of the conventional l1 norm to approximate the sparseness constraint in the wavelet domain. we combine this new constraint with subband-dependent minimization to formulate an iterative algorithm on two shift-invariant wavelet transforms, the shannon wavelet transform and dual-tree complex wavelet transform (dtcwt). this extented gsm model introduces spatially varying information into the deconvolution process and thus enables the algorithm to achieve better results with fewer iterations in our experiments.