Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

Persuading the Public : A Linguistic Analysis of Barack Obama’s Speech on “Super Tuesday” 2008

This essay examines the persuasive side of language in a speech given by Senator Barack Obama on Super Tuesday in February 2008. It studies how Senator Obama utilizes language to convince and persuade his audience. This is done from an Aristotelian point of view, meaning that the study focuses foremost on how the senator’s word choices relate to Aristotle’s three means of persuasion, ethos, pathos and logos. Those basic guiding principles are relevant to use since Aristotle’s work on the subject of rhetoric is still today one of the most relevant works in that field. The analysis is basically performed through personal observations guided by previous studies, within the frame of Aristotelian rhetoric. The results show how Senator Obama enforces the three means of persuasion through language and how it can be considered persuasive. The study might add to rhetoric studies from a linguistic perspective since it reaches a better understanding of language used in the field of politics, where rhetoric is a prominent component.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Interaction and Persuasion:An analysis of the use of rhetorical devices in Gordon Brown's speech to the Labour Party Conference, on September 25, 2006

This essay has identified and analysed rhetorical devices in Gordon Brown’s speech delivered at the Labour Party conference on September 25, 2006. The aim of the study was to identify specific rhetorical devices which are described as interactional resources, analyse their uses and discuss possible effects that they may have when included in a political speech. The results are based on my own interpretations but are supported by information provided in current literature by analysts and researchers of rhetoric use. The result findings could probably serve as evidence of the need for better understanding of the devices used by politicians in their relentless endeavours to influence audience decisions.

Filmljudets funktioner i dramafilm : En audio-visuell analys av filmen The King's Speech

Johansson, Fredrik (2012). Filmljudets funktioner i dramafilm – En audio-visuell analys av filmen The King´s Speech. Examensuppsats inom Ljudproduktion, Högskolan Dalarna, Akademin för språk och medier, Falun. I denna uppsats undersöktes filmljudet i dramafilmen The King´s Speech. Detta för att ta reda på vilka funktioner filmljudet fyller i de valda sekvenserna ur nämnda film, samt hur ljudet är placerat i filmens flerkanalsmix. Filmen granskades med hjälp av en audio-visuell analys. Denna metod går ut på att ljudet och bilden undersöks separat, för att sedan åter kombineras och analyseras som helhet. Den audio-visuella analysmetod som använts kommer från ljudteoretikern Michel Chion, och kallas Masking. Resultatet av den audio-visuella analysen pekade mot att ljudets huvudsakliga funktioner var att skapa en realistisk skildring av karaktärer och omgivningar, skapa en känsla av närvaro, samt att skapa och bibehålla olika perspektiv i den narrativa världen. Den stora majoriteten av ljud visade sig vara placerade i centerkanalen, medan främst ickediegetisk musik och ambiensljud var placerade i front- och surroundkanalerna. Detta kanalanvändande tycktes gynna de funna funktionerna, främst genom att bidra till känslan av närvaro och realism, genom att omsluta filmpubliken med ambienta ljud.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Dialectal Speech in Literature and Translation : Bachelor Degree Thesis in English Linguistics

This essay studies how dialectal speech is reflected in written literature and how this phenomenon functions in translation. With this purpose in mind, Styron's Sophie's Choice and Twain's The Adventures of Huckleberry Finn are analysed using samples of non-standard orthography which have been applied in order to reflect the dialect, or accent, of certain characters. In the same way, Lundgren's Swedish translation of Sophie's Choice and Ferres and Rolfe's Spanish version of The Adventures of Huckleberry Finn are analysed. The method consists of linguistically analysing a few text samples from each novel, establishing how dialect is represented through non-standard orthography, and thereafter, comparing the same samples with their translation into another language in order to establish whether dialectal features are visible also in the translated novels. It is concluded that non-standard orthography is applied in the novels in order to represent each possible linguistic level, including pronunciation, morphosyntax, and vocabulary. Furthermore, it is concluded that while Lundgren's translation intends to orthographically represent dialectal speech on most occasions where the original does so, Ferres and Rolfe's translation pays no attention to dialectology. The discussion following the data analysis establishes some possible reasons for the exclusion of dialectal features in the Spanish translation considered here. Finally, the reason for which this study contributes to the study of dialectology is declared.