25044094
Generalised Fuzzy Hidden Markov Models for Speech Recognition
A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were performed on the TI46 speech data corpus. A significant result for isolatedword recognition performed on a highly confusable vocabulary consisting of the nine English E-set words is that, a 33.8% recognition error rate for the HMM-based system was reduced to 30.5%, 29.9%, 29.8% and 27.8%, respectively, by using the FCM-HMM, the FE-HMM, the NC-FE-HMM, and the NC-FCM-HMM-based systems.
10506744
Fusion of Acoustic and Linguistic Features for Emotion Detection
This paper describes a system that deploys acoustic and linguistic information from speech in order to decide whether the utterance contains negative or non-negative meaning. An earlier version of this system was Passed QA to the Interspeech-2009 Emotion Challenge evaluation. The speech data consist of short utterances of the children’s speech, and the proposed system is designed to detect anger in each given chunk. Various frame-based cepstral, prosodic and acoustic features are extracted automatically and classified by means of a support vector machine. An automatic speech recognizer transcribes the utterances and yields a separate classification, based on the degree of emotional salience of the words. The emotionally salient words are computed on word hypotheses, so that un-transcribed training data is sufficient. Late fusion is applied to make a final decision on anger vs. non-anger of the utterance.
43139712
Speaker Verification with Fuzzy Fusion and Genetic Optimization
206564985
Optimizing digraph-latency based biometric typist verification systems: inter and intra typist differences in digraph latency distributions
Umphress and Williams have shown that individual differences in digraph latency may provide a means of accurately verifying the identity of computer users. The present research refined this technique by exploring inter and intra subject differences in digraph latency distributions. Experiment 1 showed that there is marked heterogeneity in the latency with which individual subjects type different digraphs. Consequently, it was found that typist verification accuracy improved when a digraph-specific index of the distance between test and reference digraph latencies was employed. Experiment 1 also showed the utility of nonlinear modelling as a tool to establish optimum verification parameter settings. Experiment 2 showed that the use of a common low-pass temporal filter cutoff setting for all typists when screening digraphs is unwise. It was found that there is a significant interaction between subjects and filter settings such that verification accuracy may improve if subject-specific filter settings are used.
35465720
Reliability score based multimodal fusion for biometric person authentication
In this paper, we propose a robust multilevel fusion strategy involving cascaded fusion of hybrid multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motiondepth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric person authentication task.
987011
Similarity normalization for speaker verification by fuzzy fusion
Abstract Similarity or likelihood normalization techniques are important for speaker verification systems as they help to alleviate the variations in the speech signals. In the conventional normalization, the a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the theory of fuzzy measure and fuzzy integral to combine the likelihood values of the cohort speakers in which the assumption of equal a priori probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. We illustrate the performance of the proposed approach by testing the speaker verification system with both the conventional and the fuzzy algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker verification system using the fuzzy integral is more flexible and more favorable than the conventional normalization method.
578051
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
47337297
probeBase: an online resource for rRNA-targeted oligonucleotide probes
Ribosomal RNA-(rRNA)-targeted oligonucleotide probes are widely used for culture-independent identification of microorganisms in environmental and clinical samples. ProbeBase is a comprehensive database containing more than 700 published rRNA-targeted oligonucleotide probe sequences (status August 2002) with supporting bibliographic and biological annotation that can be accessed through the internet at http://www.probebase.net. Each oligonucleotide probe entry contains information on target organisms, target molecule (small- or large-subunit rRNA) and position, G+C content, predicted melting temperature, molecular weight, necessity of competitor probes, and the reference that originally described the oligonucleotide probe, including a link to the respective abstract at PubMed. In addition, probes successfully used for fluorescence in situ hybridization (FISH) are highlighted and the recommended hybridization conditions are listed. ProbeBase also offers difference alignments for 16S rRNA-targeted probes by using the probe match tool of the ARB software and the latest small-subunit rRNA ARB database (release June 2002). The option to directly submit probe sequences to the probe match tool of the Ribosomal Database Project II (RDP-II) further allows one to extract supplementary information on probe specificities. The two main features of probeBase, 'search probeBase' and 'find probe set', help researchers to find suitable, published oligonucleotide probes for microorganisms of interest or for rRNA gene sequences submitted by the user. Furthermore, the 'search target site' option provides guidance for the development of new FISH probes.
26946048
Exploiting Spatial and Temporal Locality of Accesses: A New Hardware-Based Monitoring Approach for DSM Systems
The performance of a parallel system with NUMA characteristics depends on the efficient use of local memory accesses. Programming and tool environments for such DSM systems should enable and exploit data locality.
10411388
Large-scale linear programming techniques for the design of protein folding potentials
Abstract.We present large-scale optimization techniques to model the energy function that underlies the folding process of proteins. Linear Programming is used to identify parameters in the energy function model, the objective being that the model predict the structure of known proteins correctly. Such trained functions can then be used either for ab-initio prediction or for recognition of unknown structures. In order to obtain good energy models we need to be able to solve dense Linear Programming Problems with tens (possibly hundreds) of millions of constraints in a few hundred parameters, which we achieve by tailoring and parallelizing the interior-point code PCx.
751960
Performance Evaluation Methodologies for Abstract Data Type Implementation Techniques
Abstract The runtime management system for a programming language must allow the efficient manipulation of complex objects, such as instances of abstract data types (ADTs). We describe an implementation model for ADTs based on coroutine objects (i.e. as an alternative to ad hoc modifications of the runtime stack). We evaluate the performances of variants of our implementation model using two different approaches: a benchmarking technique which monitors actual costs for test programs and a “pseudo-execution” technique which estimates costs based on idealized instruction timings. The results of our comparison indicate that the two forms of evaluation give very similar cost estimates, supporting the use of the (cheaper) pseudo-execution technique. The overall performance results identify applications in which our coroutine implementation model has definite strengths and weaknesses. Finally, our implementation approach is compared with modified stack implementations used in other ADT systems.