Extending Intelligent Transportation Systems (ITS) toward suburban and peripheral regions is challenging because dense roadside infrastructure is expensive to deploy and underutilized outside peak hours. This paper proposes a V2X-enabledresting droneframework as a dynamic traffic flow management solution for ITS, in which drones equipped with Vehicle-to-Everything (V2X) connectivity are dispatched on demand to congested suburban corridors, provide temporary ITS services, and then land on attachment points to rest in a low-power state when not needed. The framework combines a synthetic multi-city road network, a time-slot–based traffic model, and a load-dependent V2X Quality-of-Service abstraction that maps latency and packet loss into an effective drone availability metric and explicitly captures the impact of non-ideal V2X conditions on control reliability. Within this framework, we develop and evaluate GOLD, a Greedy Overlap-Limited Drone deployment algorithm that prioritizes high-gain, low-overlap locations to maximize effective (overlap-removed) ITS expansion with a limited drone fleet. GOLD is compared against a conventional local threshold-based drone deployment rule that independently scales each road point’s coverage radius with traffic intensity, modeling existing overlap-unaware UAV/ITS extensions. Simulation results over multiple random map and traffic realizations show that GOLD achieves a large fraction of the baseline’s effective coverage with substantially fewer active drones under ideal V2X conditions and maintains its relative advantage when V2X latency and packet loss degrade drone availability, demonstrating that resting drones coordinated by GOLD provide a scalable and robust complement to fixed roadside ITS infrastructure.

BACKGROUND: Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. RESULTS: This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. CONCLUSIONS: CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial .

Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.

In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation approaches using matched in-domain dataset. However, adaptation would be not effective if the dataset is insufficient to estimate channel variability of the domain. In this paper, we explore the problem of performance degradation under such a situation of insufficient channel information. In order to exploit limited in-domain dataset effectively, we propose an unsupervised domain adaptation approach using Autoencoder based Domain Adaptation (AEDA). The proposed approach combines an autoencoder with a denoising autoencoder to adapt resource-rich development dataset to test domain. The proposed technique is evaluated on the Domain Adaptation Challenge 13 experimental protocols that is widely used in speaker recognition for domain mismatched condition. The results show significant improvements over baselines and results from other prior studies.

The advent of multi-core processors with a large number of cores and heterogeneous architecture poses challenges for achieving scalable cache coherence. Several recent research efforts have focused on simplifying or abandoning hardware cache coherence protocols. However, this adds a significant burden on the programmer, unless automated compiler support is developed. In this paper, we develop compiler support for parallel systems that delegate the task of maintaining cache coherence to software. Algorithms to automatically insert software cache coherence instructions into parallel applications are presented. This frees the programmer from having to manually insert coherence annotations, which can be tedious and error-prone. Experimental evaluation over a number of benchmarks demonstrates that effective compiler techniques can make software cache coherence competitive with hardware coherence schemes both in terms of energy and performance.

New architectures for extreme-scale computing need to be designed for higher energy efficiency than current systems. One recently-proposed extreme-scale many core radically simplifies the architecture, and proposes a cluster-based on-chip memory hierarchy without hardware cache coherence. To program for such an environment, this paper proposes two approaches. They use shared-memory programming either inside clusters only, or both inside and across clusters. Both approaches rely on ISA support for writeback and self-invalidation operations. Our simulation results show that hardware-incoherent cache hierarchies with our support deliver reasonable performance for applications that were not written for such hierarchies. Specifically, for execution within a cluster, the average execution time of the applications is 2% higher than with hardware cache coherence, for execution across multiple clusters, it is 5% higher than with hardware cache coherence. This is accomplished with minimal hardware support.

In this paper, robust sound source localization for surveillance system is presented. In particular, we propose an algorithm for abnormal acoustic event localization using non-negative matrix factorization based frequency bin weighting. Based on the abnormal acoustic event localization experiments in real acoustic environment, the proposed algorithm's excellent strength is validated in terms of representative performance measures compared to the conventional method.

This study proposes an effective model-based feature compensation method for robust speech recognition in background noise conditions. In the proposed scheme, an acoustic model with a phonetically constrained structure is employed for the Parallel Combined Gaussian Mixture Model (PCGMM [1]) based feature compensation method. The structure of the acoustic model includes a collection of context independent phone models. A phonetically constrained prior probability is formulated by integrating transition probability of phone models into the reconstruction procedure. Experimental results show that the PCGMM-based feature compensation employing the proposed phonetically constrained structure of acoustic model consistently outperforms the case of employing the conventional Gaussian mixture model. This demonstrates that the proposed configuration of the acoustic model is effective at improving the intelligibility of the speech reconstructed by the feature compensation method for speech recognition under diverse background noise conditions.

This study proposes an effective feature compensation-method to improve speech recognition in real-life speech conditions, where (i) severe background noise and channel distortion simultaneously exist, (ii) no development data is available, and (iii) clean data for ASR training and the latent clean speech in the test data are mismatched in the acoustic structure. The proposed feature compensation method employs an online GMM adaptation procedure which is based on MLLR, and a minimum statistics replacement technique for non-speech segments. The DARPA Tank corpus is used for performance evaluation, which includes severe real-life noisy conditions. The clean Broadcast News (BN) corpus is used for training the speech recognition system in this study. Experimental results show that the proposed feature compensation scheme outperforms GMM-based FMLLR and the ETSI AFE for DARPA Tank data, achieving a +5.56% relative improvement compared to FMLLR. These results demonstrate that the proposed feature compensation scheme is effective at improving speech recognition performance in unknown real-life adverse environments.

This study focuses on determining a procedure to select effective negative examples for development of improved Support Vector Machine (SVM) based speaker recognition. Selection of a background dataset, comprising of a group of negative examples, is critical in development of an effective decision surface between the primary speaker and outside speaker rejection space. Previous studies generally fix the number of examples based on development data for system performance evaluation, while for real applications this does not guarantee sustained performance for unseen data. In the proposed method, the error is estimated on the support vector to select the background dataset, thereby by customizing the back ground dataset for each enrollment speaker instead of training models with a fixed background data. The proposed method finds the equivalent or improved EER and DCF compared with the previous SVM-based studies, and provides consistent performance for unseen data. The method improves the 6% relative improvement on EER and DCF for NIST SRE 2010.

This paper proposes an effective feature compensation scheme to address severely adverse environments for robust speech recognition, where background noise and channel distortion are simultaneously involved. An iterative channel estimation method is integrated into the framework of our Parallel Combined Gaussian Mixture Model (PCGMM) based feature compensation algorithm [1]. A new speech corpus is generated which reflects both additive and convolutional noise corruption. The channel distortion effects are obtained from the NTIMIT and CTIMIT corpora. Evaluation of objective speech quality measures including STNR, PESQ, and speech recognition shows that the generated speech corpus represents highly challenging acoustic conditions for speech recognition. Performance evaluation of the proposed system over the obtained speech corpus demonstrates that the proposed feature compensation scheme is significantly effective at improving speech recognition performance with presence of both background noise and channel distortion, comparing to the conventional methods including the ETSI AFE. Index Terms: channel estimation, feature compensation, corpus generation, PCGMM, robust speech recognition.

This paper presents a preliminary study on an effective differentiable network service technique to achieve improved speech recognition under severely poor wireless channel conditions, by leveraging multiple priority levels applied to speech classes. Each speech class is assigned a different priority level based on its level of impact on speech recognition performance. Based on their priority level, frames of each speech class are given distinct levels of network quality of service (QoS) to satisfy the delay requirement and enable speech recognition at the receiver. This proposed Phone Impact (PI) based priority class is compared to the Voiced/Unvoiced (VU) based priority class in this study. The experimental results prove that the proposed scheme is effective at providing wireless network service for robust speech recognition under poor channel conditions, showing up to 2.67 dB and 5.93 dB lower Signal to Noise Ratio (SNR) operating regions compared to the VU based and plain protocols respectively. The PI based method also shows acceptable WERs at lower SNRs where VU and plain systems significantly degrade in speech recognition performance in case of retry limit of 6. Index Terms: Phone Impact, Priority Class, Speech Recognition, IEEE 802.11, Differentiated Maximum Retry Limit.

This paper proposes a novel mask estimation method for missing-feature reconstruction to improve speech recognition performance in various types of background noise conditions. A conventional mask estimation method based on spectral subtraction degrades performance, due to incorrect estimation of the noise signal which fails to accurately represent the variations of background noise during the incoming speech utterance. The proposed mask estimation method utilizes a Posterior-based Representative Mean (PRM) estimate for determining the reliability of the input speech spectral components, which is obtained as a weighted sum of the mean parameters of the speech model using the posterior probability. To obtain the noise-corrupted speech model, a model combination method is employed, which was proposed in our previous study for a feature compensation method. Experimental results demonstrate that the proposed mask estimation method provides more separable distributions for the reliable/unreliable component classifier compared to the conventional mask estimation method. The recognition performance is evaluated using the Aurora 2.0 framework over various types of background noise conditions and the CU-Move real-life in-vehicle corpus. The performance evaluation shows that the proposed mask estimation method is considerably more effective at increasing speech recognition performance in various types of background noise conditions, compared to the conventional mask estimation method which is based on spectral subtraction. By employing the proposed PRM-based mask estimation for missing-feature reconstruction, we obtain +23.41% and +9.45% average relative improvements in word error rate for all four types of noise conditions and CU-Move corpus, respectively, compared to conventional mask estimation methods.

This study proposes an effective angry speech detection approach by leveraging content structure within the input speech. A classifier based on an “emotional” language model score is formulated and combined with acoustic feature based classifiers including TEO-based feature and conventional Mel frequency cepstral coefficients (MFCC). The proposed detection algorithm is evaluated on real-life conversational speech which was recorded between customers and call center operators over a telephone network. Analysis on the conversational speech corpus presents a distinctive property between neutral and angry speech in word distribution and frequently occurring words. An improvement of up to 6.23% in Equal Error Rate (EER) is obtained by combining the TEO-based and MFCC features, and emotional language model score based classifiers.

This letter presents a novel confidence measure for the purpose of improving user performance in Spoken Document Retrieval (SDR). The proposed confidence measure is based on the phonetic distance between subword models, employing an anti-model which is determined to be discriminative to a target model using offline training data. As an advancement from our previous work, the proposed method employs separate phonetic similarity knowledge for vowels and consonants, resulting in more reliable performance over diverse SDR recorded speech conditions. A transcript reliability estimator is also presented, with evaluation as an application of the proposed confidence measure. Analysis on a variety of corpora including background noise, frequency band-restrictions, and a range of real-life conditions, shows that the proposed confidence measure is more reliable in detecting corrupted speech due to acoustic conditions or an unarticulated speaking style, providing a higher correlation to word error rate (WER). The proposed confidence measure is effective in increasing transcript reliability estimation performance with a 16.21% relative improvement.

This paper proposes a novel missing-feature reconstruction method to improve speech recognition in background noise environments. The existing missing-feature reconstruction method utilizes log-spectral correlation across frequency bands. In this paper, we propose to employ a temporal spectral feature analysis to improve the missing-feature reconstruction performance by leveraging temporal correlation across neighboring frames. In a similar manner with the conventional method, a Gaussian mixture model is obtained by training over the obtained temporal spectral feature set. The final estimates for missing-feature reconstruction are obtained by a selective combination of the original frequency correlation based method and the proposed temporal correlation-based method. Performance of the proposed method is evaluated on the TIMIT speech corpus using various types of background noise conditions and the CU-Move in-vehicle speech corpus. Experimental results demonstrate that the proposed method is more effective at increasing speech recognition performance in adverse conditions. By employing the proposed temporal-frequency based reconstruction method, a$+17.71\%$average relative improvement in word error rate (WER) is obtained for white, car, speech babble, and background music conditions over 5-, 10-, and 15-dB SNR, compared to the original frequency correlation-based method. We also obtain a$+16.72\%$relative improvement in real-life in-vehicle conditions using data from the CU-Move corpus.

This paper proposes a novel mask estimation method for missing-feature reconstruction to improve speech recognition performance in time-varying background noise conditions. Conventional mask estimation methods based on noise estimates and spectral subtraction fail to reliably estimate the mask. The proposed mask estimation method utilizes a posterior-based representative mean (PRM) vector for determining the reliability of the input speech spectrum, which is obtained as a weighted sum of the mean parameters of the speech model with posterior probabilities. To obtain the noise-corrupted speech model, a model combination method is employed, which was proposed in our previous study for a feature compensation method. Experimental results demonstrate that the proposed mask estimation method is considerably more effective at increasing speech recognition performance in time-varying background noise conditions. By employing the proposed PRM-based mask estimation for missing-feature reconstruction, we obtain +36.29% and +30.45% average relative improvements in WER for speech babble and background music conditions respectively, compared to conventional mask estimation methods.

This study proposes an effective angry speech detection approach employing the TEO-based feature extraction. Decorrelation processing is applied to the TEO-based feature to increase model training ability by decreasing the correlation between feature elements and vector size. Minimum classification error training is employed to increase the discrimination between the angry speech model and other stressed speech models. Combination with the conventional Mel frequency cepstral coefficients (MFCC) is also employed to leverage the effectiveness of MFCC to characterize the spectral envelope of speech signals. Experimental results over the SUSAS corpus demonstrate the proposed angry speech detection scheme is effective at increasing detection accuracy on an open-speaker and open-vocabulary task. An improvement of up to 7.78% in classification accuracy is obtained by combination of the proposed methods including decorrelation of TEO-based feature vector, discriminative training, and classifier combination.

Band-limited speech represents one of the most challenging factors for robust speech recognition. This is especially true in supporting audio corpora from sources that have a range of conditions in spoken document retrieval requiring effective automatic speech recognition. The missing-feature reconstruction method has a problem when applied to band-limited speech reconstruction, since it assumes the observations in the unreliable regions are always greater than the latent original clean speech. The approach developed here depends only on reliable components to calculate the posterior probability to mitigate the problem. This study proposes an advanced method to effectively utilize the correlation information of the spectral components across time and frequency axes in an effort to increase the performance of missing-feature reconstruction in band-limited conditions. We employ an F1 area window and cutoff border window in order to include more knowledge on reliable components which are highly correlated with the cutoff frequency band. To detect the cutoff regions for missing-feature reconstruction, blind mask estimation is also presented, which employs the synthesized band-limited speech model without secondary training data. Experiments to evaluate the performance of the proposed methods are accomplished using the SPHINX3 speech recognition engine and the TIMIT corpus. Experimental results demonstrate that the proposed time-frequency (TF) correlation based missing-feature reconstruction method is significantly more effective in improving band-limited speech recognition accuracy. By employing the proposed TF-missing feature reconstruction method, we obtain up to 14.61% of average relative improvement in word error rate (WER) for four available bandwidths with cutoff frequencies 1.0, 1.5, 2.0, and 2.5 kHz, respectively, compared to earlier formulated methods. Experimental results on the National Gallery of the Spoken Word (NGSW) corpus also show the proposed method is effective in improving band-limited speech recognition in real-life spoken document conditions.

This paper presents our recent advances for SpeechFind, a CRSS-UTD designed spoken document retrieval system for the U.S. based Collaborative Digitization Program (CDP1). A proto-type of SpeechFind for the CDP is currently serving as the search engine for 1,300 hours of CDP audio content which contain a wide range of acoustic conditions, vocabulary and period selection, and topics. In an effort to determine the amount of user corrected transcripts needed to impact automatic speech recognition (ASR) and audio search, a webbased online interface for veri..cation of ASR-generated transcripts was developed. The procedure for enhancing the transcription performance for SpeechFind is also presented. A selection of adaptation methods for language and acoustic models are employed depending on the acoustics of the corpora under test. Experimental results on the CDP corpus demonstrate that the employed model adaptation scheme using the veri..ed transcripts is effective in improving recognition accuracy. Through a combination of feature/acoustic model enhancement and language model selection, up to 24.8% relative improvement in ASR was obtained. The SpeechFind system, employing automatic transcript generation, online CDP transcript correction, and our transcript reliability estimator, demonstrates a comprehensive support mechanism to ensure reliable transcription and search for U.S. libraries with limited speech technology experience.

An effective mask estimation scheme for missing-feature reconstruction is described that achieves robust speech recognition in the presence of unknown noise. In previous work on Bayesian classification for mask estimation, white noise and colored noise were used for training mask estimators. This paper, which is concerned with both the simulation of a more diverse set of background environments and with mitigating the "sparse training" problem, describes a new Bayesian mask-estimation procedure in which each frequency band is trained independently. The new method employs colored noise for training, which is obtained by partitioning each frequency subband. We also propose a reevaluation method of voiced/unvoiced decisions to alleviate performance degradation that is caused by errors in pitch detection. Experimental results indicate that the proposed procedure in conjunction with cluster-based missing-feature imputation improves speech recognition accuracy on the Aurora 2.0 database in the presence for all types of background noise considered.

In this paper, we propose an effective mask-estimation method for missing-feature reconstruction in order to achieve robust speech recognition in unknown noise environments. In previous work, it was found that training a model for mask estimation on speech corrupted by white noise did not provide environment-independent recognition accuracy. In this paper we describe a training method based on bands of colored noise that is more effective in reflecting spectral variations across neighboring frames and subbands. We also achieved further improvement in recognition accuracy by reconsidering frames that appeared to be unvoiced in the initial pitch analysis. Performance is evaluated using the Aurora 2.0 database in the presence of various types of noise maskers. Experimental results indicate that the proposed methods are effective in estimating masks for missing-feature reconstruction while remaining more independent of the noise conditions. 1.

In this paper, we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the 'data-driven' method to PCMM for more reliable model combination and introduce a frame-synched version for estimation of environments a posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance was examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions.

This paper proposes an effective method to remedy the acoustic modeling problem inherent in the usual log-normal PMC intended for achieving robust speech recognition. In particular, the Gaussian kernels under the prescribed log-normal PMC cannot sufficiently express the corrupted speech distributions. The proposed scheme corrects this deficiency by judicially selecting the “fairly” corrupted component and by re-estimating it as a mixture of two distributions using data-driven PMC. As a result, some components become merged while equal number of components split. The determination for splitting or merging is achieved by means of measuring the similarity of corrupted speech model to those of clean model and noise model. The experimental results indicate that the suggested algorithm is effective in representing the corrupted speech distributions and attains consistent improvement over various SNR and noise cases.

This paper proposes an effective decision method focused to evaluate the “stress position”. Conventional methods usually extract the acoustic parameters and compare them to reference in absolute scale, adversely producing unstable results as testing condition changes. To cope with the environmental dependency, the proposed method is designed to be model-based that determines the stressed interval by making relative comparison over candidates. The stressed/unstressed models are then induced from normal phone models by adaptive training. The experimental results indicate that the proposed method is promising and that it is useful for automatic detection of stress positions. The results also show that generating the stressed/unstressed model by adaptive training is effective.