Julien Epps

Information theoretic measures for clusterings comparison: is a correction for chance necessary?

Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based measures. In this paper, we discuss the necessity of correction for chance for information theoretic based measures for clusterings comparison. We observe that the baseline for such measures, i.e. average value between random partitions of a data set, does not take on a constant value, and tends to have larger variation when the ratio between the number of data points and the number of clusters is small. This effect is similar in some other non-information theoretic based measures such as the well-known Rand Index. Assuming a hypergeometric model of randomness, we derive the analytical formula for the expected mutual information value between a pair of clusterings, and then propose the adjusted version for several popular information theoretic based measures. Some examples are given to demonstrate the need and usefulness of the adjusted measures.

Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction

Genomic sequence processing has been an active area of research for the past two decades and has increasingly attracted the attention of digital signal processing researchers in recent years. A challenging open problem in deoxyribonucleic acid (DNA) sequence analysis is maximizing the prediction accuracy of eukaryotic gene locations and thereby protein coding regions. In this paper, DNA symbolic-to-numeric representations are presented and compared with existing techniques in terms of relative accuracy for the gene and exon prediction problem. Novel signal processing-based gene and exon prediction methods are then evaluated together with existing approaches at a nucleotide level using the Burset/Guigo1996, HMR195, and GENSCAN standard genomic datasets. A new technique for the recognition of acceptor splice sites is then proposed, which combines signal processing-based gene and exon prediction methods with an existing data-driven statistical method. By comparison with the acceptor splice site detection method used in the gene-finding program GENSCAN, the proposed DSP-statistical hybrid technique reveals a consistent reduction in false positives at different levels of sensitivity, averaging a 43% reduction when evaluated on the GENSCAN test set.

A study of hand shape use in tabletop gesture interaction

Although manual gesture has long been suggested as an intuitive method of input for horizontal human-computer systems, little research has been conducted into observing user preferences for tabletop gesture interaction. This is particularly the case for computer vision-based gesture input, where the recognition of different hand shapes opens up a new vocabulary of interaction. In this paper, results from an observational study of manual gesture input for a tabletop display are discussed. Implications for tabletop gesture interaction design include suggestions for the use of different hands shapes for input, the desirability of combined touch screen and computer vision gesture input, and possibilities for flexible two-handed interaction.

A review of depression and suicide risk assessment using speech analysis

Review of current diagnostic and assessment methods for depression and suicidality.Review the characteristics of active depressed and suicidal speech databases.Discuss the effects of depression and suicidality on common speech characteristics.Review of studies that use speech to classify or predict depression or suicidality.Discuss future challenges in finding a speech-based markers of either condition. This paper is the first review into the automatic analysis of speech for use as an objective predictor of depression and suicidality. Both conditions are major public health concerns; depression has long been recognised as a prominent cause of disability and burden worldwide, whilst suicide is a misunderstood and complex course of death that strongly impacts the quality of life and mental health of the families and communities left behind. Despite this prevalence the diagnosis of depression and assessment of suicide risk, due to their complex clinical characterisations, are difficult tasks, nominally achieved by the categorical assessment of a set of specific symptoms. However many of the key symptoms of either condition, such as altered mood and motivation, are not physical in nature; therefore assigning a categorical score to them introduces a range of subjective biases to the diagnostic procedure. Due to these difficulties, research into finding a set of biological, physiological and behavioural markers to aid clinical assessment is gaining in popularity. This review starts by building the case for speech to be considered a key objective marker for both conditions; reviewing current diagnostic and assessment methods for depression and suicidality including key non-speech biological, physiological and behavioural markers and highlighting the expected cognitive and physiological changes associated with both conditions which affect speech production. We then review the key characteristics; size, associated clinical scores and collection paradigm, of active depressed and suicidal speech databases. The main focus of this paper is on how common paralinguistic speech characteristics are affected by depression and suicidality and the application of this information in classification and prediction systems. The paper concludes with an in-depth discussion on the key challenges - improving the generalisability through greater research collaboration and increased standardisation of data collection, and the mitigating unwanted sources of variability - that will shape the future research directions of this rapidly growing field of speech processing research.

On DNA Numerical Representations for Period-3 Based Exon Prediction

Processing of DNA sequences using traditional digital signal processing methods requires their conversion from a character string into numerical sequences as a first step. Many representations introduced previously assign values to indicate the four DNA nucleotides A, C, G, and T that impose mathematical structures not present in the actual DNA sequence. In this paper, almost all existing methods are compared for the purpose of identifying protein coding regions, using the discrete Fourier transform (DFT) based spectral content measure to exploit period-3 behaviour in the exonic regions for the GENSCAN test set. False positive vs. sensitivity, receiver operating characteristic (ROC) curve and exonic nucleotides detected as false positive results all show that the two newly proposed numerical of DNA representations perform better than the well-known Z-curve, tetrahedron, and Voss representations, with 66-75% less processing. By comparison with Voss representation, the proposed paired numeric method can produce relative improvements of up to 12% in terms of prediction accuracy of exonic nucleotides at a 10% false positive rate using the GENSCAN test set.

Speaker verification using sparse representation classification

Sparse representations of signals have received a great deal of attention in recent years, and the sparse representation classifier has very lately appeared in a speaker recognition system. This approach represents the (sparse) GMM mean supervector of an unknown speaker as a linear combination of an over-complete dictionary of GMM supervectors of many speaker models, and ℓ1-norm minimization results in a non-zero coefficient corresponding to the unknown speaker class index. Here this approach is tested on large databases, introducing channel-/session-variability compensation, and fused with a GMM-SVM system. Evaluations on the NIST 2001 SRE and NIST 2006 SRE database show that when the outputs of the MFCC UBM-GMM based classifier (for NIST 2001 SRE) or MFCC GMM-SVM based classifier (for NIST 2006 SRE) are fused with the MFCC GMM-Sparse Representation Classifier (GMM-SRC) based classifier, an absolute gain of 1.27% and 0.25% in EER can be achieved respectively.

Automatic classification of eye activity for cognitive load measurement with emotion interference

Measuring cognitive load changes can contribute to better treatment of patients, can help design effective strategies to reduce medical errors among clinicians and can facilitate user evaluation of health care information systems. This paper proposes an eye-based automatic cognitive load measurement (CLM) system toward realizing these prospects. Three types of eye activity are investigated: pupillary response, blink and eye movement (fixation and saccade). Eye activity features are investigated in the presence of emotion interference, which is a source of undesirable variability, to determine the susceptibility of CLM systems to other factors. Results from an experiment combining arithmetic-based tasks and affective image stimuli demonstrate that arousal effects are dominated by cognitive load during task execution. To minimize the arousal effect on CLM, the choice of segments for eye-based features is examined. We then propose a feature set and classify three levels of cognitive load. The performance of cognitive load level prediction was found to be close to that of a reaction time measure, showing the feasibility of eye activity features for near-real time CLM.

A novel instrument to measure acoustic resonances of the vocal tract during phonation

Acoustic resonances of the vocal tract give rise to formants (broad bands of acoustic power) in the speech signal when the vocal tract is excited by a periodic signal from the vocal folds. This paper reports a novel instrument which uses a real-time, non-invasive technique to measure these resonances accurately during phonation. A broadband acoustic current source is located just outside the mouth of the subject and the resulting acoustic pressure is measured near the lips. The contribution of the speech signal to the pressure spectrum is then digitally suppressed and the resonances are calculated from the input impedance of the vocal tract as a function of the frequency. The external excitation signal has a much smaller harmonic spacing than does the periodic signal from the vocal folds and consequently the resonances are determined much more accurately due to the closer sampling. This is particularly important for higher pitched voices and we demonstrate that this technique can be markedly superior to the curve-fitting technique of linear prediction. The superior frequency resolution of this instrument which results from external vocal tract excitation can provide the precise, stable, effective, articulatory feedback considered essential for some language-learning and speech-therapy applications.

Diagnosis of depression by behavioural signals: a multimodal approach

Quantifying behavioural changes in depression using affective computing techniques is the first step in developing an objective diagnostic aid, with clinical utility, for clinical depression. As part of the AVEC 2013 Challenge, we present a multimodal approach for the Depression Sub-Challenge using a GMM-UBM system with three different kernels for the audio subsystem and Space Time Interest Points in a Bag-of-Words approach for the vision subsystem. These are then fused at the feature level to form the combined AV system. Key results include the strong performance of acoustic audio features and the bag-of-words visual features in predicting an individuals level of depression using regression. Interestingly, in the context of the small amount of literature on the subject, is that our feature level multimodal fusion technique is able to outperform both the audio and visual challenge baselines.

Eye activity as a measure of human mental effort in HCI

The measurement of a users mental effort is a problem whose solutions may have important applications to adaptive interfaces and interface evaluation. Previous studies have empirically shown links between eye activity and mental effort; however these have usually investigated only one class of eye activity on tasks atypical of HCI. This paper reports on research into eight eye activity based features, spanning eye blink, pupillary response and eye movement information, for real time mental effort measurement. Results from an experiment conducted using a computer-based training system show that the three classes of eye features are capable of discriminating different cognitive load levels. Correlation analysis between various pairs of features suggests that significant improvements in discriminating different effort levels can be made by combining multiple features. This shows an initial step towards a real-time cognitive load measurement system in human-computer interaction.