The Effects Of Song Singing On Improvements In Affective Intonation Of People With Traumatic Brain Injury

This study examined the effects of a song-singing program on the affective speaking intonation of traumatically brain-injured people who presented with monotonal voices. Four subjects received 15 sessions of music therapy comprising the singing of three subject-preferred songs. The variables of: speaking fundamental frequency, standardised variability and slope, pitch range, pitch-matching accuracy and mood were analysed pre and post-session. The audio data was analysed using Multi-speech with real-time pitch module. A key phrase selected from songs used in the subjects' sessions was also analysed to determine pitch-matching accuracy and later compared with the intervals in the pitch-matching exercise. The visual analogue mood scales were assessed as per standardised procedure. Results suggest that long-term improvements in affective intonation are evident, especially in fundamental frequency, however the response direction and degree of change are idiosyncratic. Immediate treatment effects (pre/post-session differences) were in the direction contrary to that expected (negative). Fatigue is suggested as one explanation for this result, particularly as fatigue was reported in the visual analogue mood scale. Vocal range improved over time in all four subjects and was positively correlated with all three intonation components, particularly the standardised variability score. High variability in responses was evident in the interval tasks. The mood scale responses were also variable, and interpretations of therapy effects on mood should be treated with caution. Negative correlations (that opposite to expected) were found between the mood scales and intonation variables suggesting that as subjects reported becoming more emotional, they became more monotonal, had flatter slope measures and a lower fundamental frequency. Subjects sang intervals more accurately when they were in a song than when presented in isolation

Robust Phase-based Speech Signal Processing From Source-Filter Separation to Model-Based Robust ASR

The Fourier analysis plays a key role in speech signal processing. As a complex quantity, it can be expressed in the polar form using the magnitude and phase spectra. The magnitude spectrum is widely used in almost every corner of speech processing. However, the phase spectrum is not an obviously appealing start point for processing the speech signal. In contrast to the magnitude spectrum whose fine and coarse structures have a clear relation to speech perception, the phase spectrum is difficult to interpret and manipulate. In fact, there is not a meaningful trend or extrema which may facilitate the modelling process. Nonetheless, the speech phase spectrum has recently gained renewed attention. An expanding body of work is showing that it can be usefully employed in a multitude of speech processing applications. Now that the potential for the phase-based speech processing has been established, there is a need for a fundamental model to help understand the way in which phase encodes speech information. In this thesis a novel phase-domain source-filter model is proposed that allows for deconvolution of the speech vocal tract (filter) and excitation (source) components through phase processing. This model utilises the Hilbert transform, shows how the excitation and vocal tract elements mix in the phase domain and provides a framework for efficiently segregating the source and filter components through phase manipulation. To investigate the efficacy of the suggested approach, a set of features is extracted from the phase filter part for automatic speech recognition (ASR) and the source part of the phase is utilised for fundamental frequency estimation. Accuracy and robustness in both cases are illustrated and discussed. In addition, the proposed approach is improved by replacing the log with the generalised logarithmic function in the Hilbert transform and also by computing the group delay via regression filter. Furthermore, statistical distribution of the phase spectrum and its representations along the feature extraction pipeline are studied. It is illustrated that the phase spectrum has a bell-shaped distribution. Some statistical normalisation methods such as mean-variance normalisation, Laplacianisation, Gaussianisation and Histogram equalisation are successfully applied to the phase-based features and lead to a significant robustness improvement. The robustness gain achieved through using statistical normalisation and generalised logarithmic function encouraged the use of more advanced model-based statistical techniques such as vector Taylor Series (VTS). VTS in its original formulation assumes usage of the log function for compression. In order to simultaneously take advantage of the VTS and generalised logarithmic function, a new formulation is first developed to merge both into a unified framework called generalised VTS (gVTS). Also in order to leverage the gVTS framework, a novel channel noise estimation method is developed. The extensions of the gVTS framework and the proposed channel estimation to the group delay domain are then explored. The problems it presents are analysed and discussed, some solutions are proposed and finally the corresponding formulae are derived. Moreover, the effect of additive noise and channel distortion in the phase and group delay domains are scrutinised and the results are utilised in deriving the gVTS equations. Experimental results in the Aurora-4 ASR task in an HMM/GMM set up along with a DNN-based bottleneck system in the clean and multi-style training modes confirmed the efficacy of the proposed approach in dealing with both additive and channel noise

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

Understanding functional cognitive disorder phenotypes in the differential diagnosis of neurodegenerative disease

Increasing numbers of people seek medical help for worrying cognitive symptoms. However, many patients attending services designed to detect neurodegenerative disease (such as memory clinics) do not have evidence of neurodegenerative disease, nor do their symptoms progress as such. In some, alternative causes are identified, such as medication or systemic illness. Others have been described as ‘worried well’, as having symptoms driven by anxiety and depression, or else reassured that they have no disease. These patients, many of whom have functional cognitive disorders, have been poorly served by research and as a result there is little evidence to guide effective treatment. Functional cognitive disorders are an important group of overlapping conditions in which cognitive symptoms are experienced as the result of reversible and inconsistent disturbances of attention and abnormal metacognitive interpretation. They have been neglected in functional disorder research and in neurodegenerative disease research, where they are an important differential diagnosis. The aims of this PhD were to build a firm definition of functional cognitive disorders, and to justify and explain how this definition might relate to previous and current diagnostic terminologies; to examine prevalence; to understand clinical associations; and to develop clinical methods to support accurate clinical diagnosis. This thesis investigates the terminologies and theoretical models that have previously been used to describe and explain functional cognitive disorders; systematically reviews prevalence and clinical features; describes comparative studies of healthy adults and simulators, and systematically reviews diagnostic performance of traditional psychometric tests of inconsistency (validity tests) in order to develop understanding of functional cognitive disorder mechanism and potential diagnostic methods. Finally, the thesis includes a clinical study of adults with cognitive symptoms, describing novel diagnostic techniques with wide potential utility