waashk/dblp huggingface dataset:
large margin semi-tied covariance transforms for discriminative training. we discuss the applicability of large margin techniques to the problem of estimating linear transforms for discriminative training of a semi-tied covariance (stc) model. since stc models are good proxies for full-covariance (fc) gaussian models, the idea is to combine the benefit of the latest discriminative training techniques and the modeling advantage of fc gaussians at a much lower computational cost. we study the interaction of these transforms with feature-space and model-space discriminative training on state-of-the-art speaker adapted systems built for a large-scale arabic broadcast news transcription task.
exploiting t-junctions for depth segregation in single images. occlusion is one of the major consequences of the physical image generation process: it occurs when an opaque object partly obscures the view of another object further away from the viewpoint. local signatures of occlusion in the projected image plane are t-shaped junctions. they represent, in some sense, one of the most primitive depth information. in this paper, we investigate the usefulness of t-junctions for depth segregation in single images. our strategy consists in incorporating ordering information provided by t-junctions into a region merging algorithm and then reasoning about the depth relations between the regions of the final partition using a graph model. experimental results demonstrate the effectiveness of the proposed approach.
voice conversion based on simultaneous modelling of spectrum and f0. this paper proposes a simultaneous modeling of spectrum and f0 for voice conversion based on msd (multi-space probability distribution) models. as a conventional technique, a spectral conversion based on gmm (gaussian mixture model) has been proposed. although this technique converts spectral feature sequences nonlinearly based on gmm, f0 sequences are usually converted by a simple linear function. this is because f0 is undefined in unvoiced segments. to overcome this problem, we apply msd models. the msd-gmm allows to model continuous f0 values in voiced frames and a discrete symbol representing unvoiced frames within an unified framework. furthermore, the msd-hmm is adopted to model long term correlations in f0 sequences.
independent component analysis for noisy speech recognition. independent component analysis (ica) is not only popular for blind source separation but also for unsupervised learning when the observations can be decomposed into some independent components. these components represent the specific speaker, gender, accent, noise or environment, and act as the basis functions to span the vector space of the human voices in different conditions. different from eigenvoices built by principal component analysis, the proposed independent voices are estimated by ica algorithm, and are applied for efficient coding of an adapted acoustic model. since the information redundancy is significantly reduced in independent voices, we effectively calculate a coordinate vector in independent voice space, and estimate the hidden markov models (hmms) for speech recognition. in the experiments, we build independent voices from hmms under different noise conditions, and find that these voices attain larger redundancy reduction than eigenvoices. the noise adaptive hmms generated by independent voices achieve better recognition performance than those by eigenvoices.
statistical dialog management applied to wfst-based dialog systems. we have proposed an expandable dialog scenario description and platform to manage dialog systems using a weighted finite-state transducer (wfst) in which user concept and system action tags are input and output of the transducer, respectively. in this paper, we apply this framework to statistical dialog management in which a dialog strategy is acquired from a corpus of human-to-human conversation for hotel reservation. a scenario wfst for dialog management was automatically created from an n-gram model of a tag sequence that was annotated in the corpus with interchange format (if). additionally, a word-to-concept wfst for spoken language understanding (slu) was obtained from the same corpus. the acquired scenario wfst and slu wfst were composed together and then optimized. we evaluated the proposed wfst-based statistic dialog management in terms of correctness to detect the next system actions and have confirmed the automatically acquired dialog scenario from a corpus can manage dialog reasonably on the wfst-based dialog management platform.
speaker identification with whispered speech based on modified lfcc parameters and feature mapping. much research recently in speaker recognition has been devoted to robustness due to microphone and channel effects. however, changes in vocal effort, especially whispered speech, present significant challenges in maintaining system performance. due to the absence of any periodic excitation in whisper, the spectral structure in whisper and neutral speech will differ. therefore, performance of speaker id systems, trained mainly with high energy voiced phonemes, degrades when tested with whisper. this study considers a front-end feature compensation method for whispered speech to improve speaker recognition using a neutral trained system. first, an alternative feature vector with linear frequency cepstral coefficients (lfcc) is introduced based on spectral analysis from both speech modes. next, for the first time a feature mapping is proposed for reducing whisper/neutral mismatch in speaker id. feature mapping is applied on a frame-by-frame basis between two speaker independent gmms (gaussian mixture models) of whispered and neutral speech. text independent closed set speaker id results show an absolute 20% improvement in accuracy when compared with a traditional mfcc feature based system. this result confirms a viable approach to improving speaker id performance between neutral and whispered speech conditions.
blind source extraction of periodic signals. a new algorithm is developed here for blind extraction of periodic signals. it is assumed that the fundamental frequencies of the sources (or alternatively one of the harmonics for each source) are known a priori. necessary and sufficient conditions for blind source extraction of cyclostationary signals are introduced and the optimization problem is solved using steepest descent method for complex matrices. computer simulation results verify the effectiveness and good performance of the algorithm.
optimal inference of the inverse gamma texture for a compound-gaussian clutter. we first derive the stochastic dynamics of a gaussian-compound model with an inverse gamma distributed texture from jakeman's random walk model with step number fluctuations. following a similar approach existing for the k-distribution, we show how the scattering cross-section may be inferred from the fluctuations of the scattered field intensity. by discussing the sources of discrepancy arising during this process, we derive an analytical expression for the inference error based on its asymptotic behaviours, together with a condition to minimize it. simulated data enables verification of our proposed technique. the interest of this strategy is discussed in the context of radar applications.
ranging energy optimization for robust sensor positioning. we address ranging energy optimization for an unsynchronized localization system, which features robust sensor positioning, in the sense that specific accuracy requirements are fulfilled within a prescribed service area. optimization problems related to the ranging energy of a sensor and beacons are proposed, after which a practical algorithm based on semidefinite programming is presented. the effectiveness of the algorithm is illustrated by a numerical experiment.
maximin robust design for mimo communication systems against imperfect csit. this paper considers robust transmit strategies, against the imperfectness of csit, for mimo communication systems. following a deterministic model that assumes the actual channel inside an ellipsoid centered at a nominal channel, we maximize the worst-case received snr. it is shown that, for a general class of power constraints, the resulting maximin problem can be equivalently transformed into a convex problem, or even further into a semidefinite program. the most important result is that the optimal transmit directions are just the right singular vectors of the nominal channel under some mild conditions. this result reduces the complicated matrix-valued problems to scalar power allocation problems, for which the closed-form solutions are provided.
an adaptive quantization scheme for distributed consensus. the problem of distributed average consensus with quantized data is considered in this paper. we firstly propose a simple modification to the classical consensus protocol. under a condition that the quantization noise variance converges to zero, the proposed protocol achieves a consensus in a mean squared sense and the consensus value is equal to the average of the initial state. based on this result, we develop an adaptive quantization scheme which can adaptively adjust its quantization threshold and step-size by learning from previous runs, in a way such that the quantization noise variance at each sensor decreases to zero. simulation results are presented to illustrate the effectiveness of the proposed algorithm.
auditory coding based speech enhancement. this paper demonstrates a speech enhancement system based on an efficient auditory coding approach, coding of time-relative structure using spikes. the spike coding method can more compactly represent the non-stationary characteristics of speech signals than the fourier transform or wavelet transform. enhancement is accomplished through the use of mmse thresholding on the spike code. experimental results show that compared with the spectral domain logstsa filter, both the subjective spectrogram evaluation and objective ssnr improvement for the proposed approach is better in suppressing noise in high noise situations, with fewer musical artifacts.
erp source reconstruction by using particle swarm optimization. localization of the sources of event related potentials (erp) is a challenging inverse problem, especially to resolve sources of neural activity occurring simultaneously. by using an effective dipole source model, we propose a new technique for accurate source localization of erp signals. the parameters of the dipole erp sources are optimally chosen by using particle swarm optimization technique. obtained results on synthetic data sets show that proposed method well localizes the dipoles on their actual locations. on real data sets, the fit error between the actual and reconstructed data is successfully reduced to noise level by localizing a few dipoles in the brain.
the data deluge: challenges and opportunities of unlimited data in statistical signal processing. recently, there has been a dramatic increase of the amount of audio, video, and images created and shared on the internet by users around the world. much of this content is publicly available and free of cost. when viewed through the lens of pattern classification, this content can be seen as a virtually unlimited supply of training data for various statistical modeling and labeling tasks such as speech recognition and computer vision. in order to effectively exploit this data resource, significant research challenges must be addressed. in this paper, we present three significant challenges that must be solved to harness the potential of this “data deluge”. we then describe recent work in spoken language processing and image processing that has begun to address these challenges in order to tackle large-scale classification tasks. by bringing together the work of these two communities, we hope to stimulate the cross-pollination of ideas and methods among different signal processing communities.
emotion recognition from speech: putting asr in the loop. this paper investigates the automatic recognition of emotion from spoken words by vector space modeling vs. string kernels which have not been investigated in this respect, yet. apart from the spoken content directly, we integrate part-of-speech and higher semantic tagging in our analyses. as opposed to most works in the field, we evaluate the performance with an asr engine in the loop. extensive experiments are run on the fau aibo emotion corpus of 4k spontaneous emotional child-robot interactions and show surprisingly low performance degradation with real asr over transcription-based emotion recognition. in the result, bag of words dominate over all other modeling forms based on the spoken content.
design of robust superdirective beamformers as a convex optimization problem. broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative to the wavelengths. superdirective beamformers are extremely sensitive to spatially white noise and to small errors in the array characteristics. these errors are nearly uncorrelated from sensor to sensor and affect the beamformer in a manner similar to spatially white noise. hence the white noise gain (wng) is a commonly used measure for the robustness of beamformer designs. in this paper, we present a method which incorporates a constraint for the wng into a least-squares beamformer design and still leads to a convex optimization problem that can be solved directly, e.g. by sequential quadratic programming. the effectiveness of this method is demonstrated by design examples.
image deblocking using dual adaptive fir wiener filter in the dct transform domain. blocking artifacts exist in images and video sequences compressed to low bit rates using block discrete cosine transform (dct) compression standards. in order to reduce blocking artifacts, a novel dct domain technique is presented in this paper. firstly, a new fir wiener filter which exploits the dependence of neighboring dct coefficients based on the linear minimum mean-square-error (lmmse) criterion is proposed. then we apply the new fir wiener filter twice in a dual adaptive filtering structure to restore each quantized dct coefficient. in addition, an efficient parameter estimation method is proposed for the designed filter. experimental results show that the performance of the proposed method is comparable to the state-of-the-art methods but has low computational complexity.
optimal transmission codebook design in fading channels for the decentralized estimation in wireless sensor networks. in this paper, we design the near optimal transmission codebook for decentralized estimation in wireless sensor networks with uniform quantizations and digital communications over orthogonal rayleigh fading channels. we start from deriving the maximum likelihood estimator (mle) for arbitrary transmission codebook with known and unknown channel state information (csi) at the fusion center (fc). because the mle is not a convex problem in the system we considered, it is not trivial to obtain an analytical expression of either its mean square error or cramer-rao lower bound (crlb). by analyzing the likelihood function, we convert the optimal transmission codebook design minimizing the crlb to a two-stage optimization problem, where the problem in each stages is convex. it is shown that the estimation accuracy with our designed codebooks and mle is superior to that with available transmission schemes. the proposed codebook suggests that the sensors should use orthogonal codes to transmit different observations for unknown csi, while the optimal codebook for known csi is not orthogonal.
a new hardware implementation of the h.264 8×8 transform and quantization. h.264/avc is the most powerful technology in video compression/transmission area because of its high coding efficiency and robustness. in this paper, we propose a new hardware architecture of 8×8 integer transform and quantization for h.264 which promises very low resource utilization. in the architecture, each pixel is processed one by one on a simplified pipeline without multiplication. thus, redundant modules, which are used for block-based or row-based parallel processing, can be reduced. experimental results show that it can reduce resource usage 30% compared to previously proposed models. it can be used for mobile applications. it covers a wide range of parameters as well.
experimenting with a global decision tree for state clustering in automatic speech recognition systems. in modern automatic speech recognition systems, it is standard practice to cluster several logical hidden markov model states into one physical, clustered state. typically, the clustering is done such that logical states from different phones or different states can not share the same clustered state. in this paper, we present a collection of experiments that lift this restriction. the results show that, for aurora 2 and aurora 3, much smaller models perform as least as well as the standard baseline. on a timit phone recognition task, we analyze the tying structures introduced, and discuss the implications for building better acoustic models.
improving multi-lattice alignment based spoken keyword spotting. in previous work, we showed that using a lattice instead of the 1-best path to represent both the query and the utterance being searched is beneficial for spoken keyword spotting. in this paper, we introduce several techniques that further improve our multi-lattice alignment approach, including edit operation modeling and supervised training of the conditional probability table, something which cannot be directly trained by traditional maximum likelihood estimation. experiments on timit show that the proposed methods significantly improve the performance of spoken keyword spotting.
sparse beamforming for active underwater electrolocation. weakly electric fish have the ability to navigate and locate prey in the dark using a unique weak electrosense system. imitating that ability of electric fish, we develop an electric-field sensing system capable of high-resolution imaging of the surrounding environment, by use of a novel beamforming technique that exploits the sparsity of sources in a scanned space. both simulation and experimental results show that the sparse beamforming technique accurately images not only a single object but also multiple objects with highly correlated signals.
top-down image segmentation using the mumford-shah functional and level set image representation. a top-down image segmentation method is proposed in this paper, utilizing level set image representation and the piecewise-constant mumford-shah functional. the method achieves top-down hierarchical segmentation by taking advantage of the tree structure provided by level set image representation. the piecewise-constant mumford-shah functional is utilized in the proposed method to determine if each node in the tree segments the image. experimental results show that this method is able to segment complicated real images. 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
optimal design of hybrid filterbank analog-to-digital converters using input statistics. the hybrid filterbank architecture permits implementing accurate, high speed analog-to-digital converters. however, its design is technically involved since perfect reconstruction of the desired samples cannot be achieved in general. in this paper we propose a design method which generalizes existing approaches by dropping the bandlimited assumption on the input signal. the design minimizes the power of the reconstruction error in the samples, for a given input signal power spectrum. we discuss the use of blind techniques to estimate the analysis filterbank parameters as well as the input power spectrum, and we present simulation results to demonstrate the clear advantage of the proposed design, even under input spectrum uncertainties.
a parallel architecture for 3gpp2/umb turbo interleavers. in this paper, an efficient architecture for a parallel pruned turbo interleaver for 3gpp2/umb physical layer standard [1] is presented. turbo interleaving in umb turbo codes is based on filling a 2d array row by row, interleaving each row using a linear congruential sequence, bit-reversing the order of the rows, and then reading the interleaved addresses column by column. pruning creates a serial bottleneck since the interleaved address of a linear address x is a function of the number of pruned addresses up to x. an architecture based on the parallel lookahead pruned interleaving algorithm proposed in [2] is presented. the algorithm breaks this dependency and interleaves any address in o(log2 x) steps by enabling a parallel turbo interleaver design with a desired degree of parallelism. the architecture can be implemented efficiently in hardware using basic arithmetic building blocks.
detection of stop landmarks using gaussian mixture modeling of speech spectrum. perception of speech under adverse listening conditions may be improved by processing it to incorporate properties of clear speech. it needs automated detection of stop landmarks and enhancement of bursts and transition segments. a technique for accurate detection of stop landmarks in continuous speech based on parameters derived from gaussian mixture modeling of log magnitude spectrum, a voicing onset-offset detector, and a spectral flatness measure is presented. applying the technique on sentences from the timit database resulted in burst detection rates of 98, 97, 95, 90, and 73 % at temporal accuracies of 30, 20, 15, 10, and 5 ms respectively.
secure exp-golomb coding using stream cipher. in this paper, we propose a secure exp-golomb coding scheme by incorporating with a stream cipher. different from the traditional case of using stream cipher where the key stream is directly xored with the plaintext, we here use the key stream to control the switching between two coding conventions (leading zeros and leading ones). security analysis results show that the proposed system can provide high level of security with the same coding efficiency and negligible additional cost, compared with a regular exp-golomb coding. this scheme could potentially be applied to the state-of-the-art multimedia compression systems, e.g., h. 264, to offer security features.
combining discriminative re-ranking and co-training for parsing mandarin speech transcripts. discriminative reranking has been able to significantly improve parsing performance, and co-training has proven to be an effective weakly supervised learning algorithm to bootstrap parsers from a small in-domain seed labeled corpus using a large amount of unlabeled in-domain data. in this paper, we present systematic investigations on combining discriminative reranking and co-training, including co-training reranked parsers and co-training rerankers. we show that combining discriminative reranking and co-training could improve the f-measure by 1.8%-2% absolute compared to co-training two state-of-the-art chinese parsers without reranking, for parsing mandarin broadcast news and conversation transcripts. 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