11 resultados para Parties of speech

em Dalarna University College Electronic Archive


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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.

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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.

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Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

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This chapter is an analysis of a 100,000-word corpus consisting of message-board postings on hip-hop websites. A discourse analysis of this corpus reveals three strategies employed by the posters to identify themselves as members of the hip-hop community in the otherwise anonymous setting of the internet: (1) defined openings and closings, (2) repeated use of slang and taboo terms, and (3) performance of verbal art. Each strategy is characterized by the codification of non-standard grammar and pronunciations characteristic of speech, as well as by the use of non-standard orthography. The purpose of the discourse is shown to be a performance of identity, whereby language is used and recognized as the discursive construction of one’s hip-hop identity.

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The narrative of the United States is of a "nation of immigrants" in which the language shift patterns of earlier ethnolinguistic groups have tended towards linguistic assimilation through English. In recent years, however, changes in the demographic landscape and language maintenance by non-English speaking immigrants, particularly Hispanics, have been perceived as threats and have led to calls for an official English language policy.This thesis aims to contribute to the study of language policy making from a societal security perspective as expressed in attitudes regarding language and identity originating in the daily interaction between language groups. The focus is on the role of language and American identity in relation to immigration. The study takes an interdisciplinary approach combining language policy studies, security theory, and critical discourse analysis. The material consists of articles collected from four newspapers, namely USA Today, The New York Times, Los Angeles Times, and San Francisco Chronicle between April 2006 and December 2007.Two discourse types are evident from the analysis namely Loyalty and Efficiency. The former is mainly marked by concerns of national identity and contains speech acts of security related to language shift, choice and English for unity. Immigrants are represented as dehumanised, and harmful. Immigration is given as sovereignty-related, racial, and as war. The discourse type of Efficiency is mainly instrumental and contains speech acts of security related to cost, provision of services, health and safety, and social mobility. Immigrants are further represented as a labour resource. These discourse types reflect how the construction of the linguistic 'we' is expected to be maintained. Loyalty is triggered by arguments that the collective identity is threatened and is itself used in reproducing the collective 'we' through hegemonic expressions of monolingualism in the public space and semi-public space. The denigration of immigrants is used as a tool for enhancing societal security through solidarity and as a possible justification for the denial of minority rights. Also, although language acquisition patterns still follow the historical trend of language shift, factors indicating cultural separateness such as the appearance of speech communities or the use of minority languages in the public space and semi-public space have led to manifestations of intolerance. Examples of discrimination and prejudice towards minority groups indicate that the perception of worth of a shared language differs from the actual worth of dominant language acquisition for integration purposes. The study further indicates that the efficient working of the free market by using minority languages to sell services or buy labour is perceived as conflicting with nation-building notions since it may create separately functioning sub-communities with a new cultural capital recognised as legitimate competence. The discourse types mainly represent securitising moves constructing existential threats. The perception of threat and ideas of national belonging are primarily based on a zero-sum notion favouring monolingualism. Further, the identity of the immigrant individual is seen as dynamic and adaptable to assimilationist measures whereas the identity of the state and its members are perceived as static. Also, the study shows that debates concerning language status are linked to extra-linguistic matters. To conclude, policy makers in the US need to consider the relationship between four factors, namely societal security based on collective identity, individual/human security, human rights, and a changing linguistic demography, for proposed language intervention measures to be successful.

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This paper studies one of the recurrent topics of writing found in Amélie Nothomb’snovels: beauty and ugliness. The novels Mercure and Attentat are analyzed in detail,with respect to figures of speech used to describe the extreme physical appearance ofthe protagonists and the role of the duality beauty-ugliness in the advancement of theplot.

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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.

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Abstract. In addition to 9 vowel and 18 consonant phonemes, Swedish has three prosodic phonemic contrasts: word stress, quantity and tonal word accent. There are also examples of distinctive phrase or sentence stress, where a verb can be followed by either an unstressed preposition or a stressed particle. This study focuses on word level and more specifically on word stress and tonal word accent in disyllabic words. When making curriculums for second language learners, teachers are helped by knowing which phonetic or phonological features are more or less crucial for the intelligibility of speech and there are some structural and anecdotal evidence that word stress should play a more important role for intelligibility of Swedish, than the tonal word accent. The Swedish word stress is about prominence contrasts between syllables, mainly signaled by syllable duration, while the tonal word accent is signaled mainly by pitch contour. The word stress contrast, as in armen [´arːmən] ‘the arm’ - armén [ar´meːn] ‘the army’, the first word trochaic and the second iambic, is present in all regional varieties of Swedish, and realized with roughly the same acoustic cues, while the tonal word accent, as in anden [´anːdən] ‘the duck’ - anden [`anːdən] ‘the spirit’ is absent in some dialects (as well as in singing), and also signaled with a variety of tonal patterns depending on region. The present study aims at comparing the respective perceptual weight of the two mentioned contrasts. Two lexical decision tests were carried out where in total 34 native Swedish listeners should decide whether a stimulus was a real word or a non-word. Real words of all mentioned categories were mixed with nonsense words and words that were mispronounced with opposite stress pattern or opposite tonal word accent category. The results show that distorted word stress caused more non-word judgments and more loss, than distorted word accent. Our conclusion is that intelligibility of Swedish is more sensitive to distorted word stress pattern than to distorted tonal word accent pattern. This is in compliance with the structural arguments presented above, and also with our own intuition.

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随着社会的发展,尤其是互联网的发展,很多语言每年都涌现出了不少新词汇。词语是每个语言最基本也是最重要的组成部分,因此分析这些新词汇的结构特点以及构词法是很有意义的。这篇文章分析了2014年出现在中文里的新词汇和它们的构词方式,论文的目的是为了更好地了解中文词汇的发展和特点。本文以《2014汉语新词语》中公布的2014年出现的新词汇作为语料进行分析,发现了以下两个主要特点:第一,合成法,派生法,缩略法是2014年产生的新词汇的主要构词方式;第二, 百分之七十二的新词汇是多音节词(包含三个或者三个以上音节),而百分之八十的是名词。这些特点说明中文词汇现阶段的特点和发展趋势,跟传统的中文词汇有不同之处。

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Ce mémoire est constitué par une analyse écocritique du roman Naissance d'un pont, écrit par Maylis de Kerangal. Le but général du mémoire est d'examiner comment ce roman décrit la relation entre l'homme et la nature. Nous examinons surtout les stratégies narratives employées par l'auteure pour transmettre l'image de cette relation et en plus, nous discutons le rôle joué par les différents paysages se retrouvant dans le récit. Finalement, l'analyse comprend aussi une brève réflexion sur la capacité éventuelle du roman d'influencer l'attitude du lecteur envers l'écologie. L'analyse des stratégies narratives se concentre sur le rôle du narrateur, ainsi que sur la présence et la fonction des perspectives éthiques, des noms symboliques et des figures de style. Ces stratégies contribuent à dépeindre une variété d'idées par rapport aux modes de vie de la société humaine, alors que les descriptions des paysages démontrent la relation complexe entre cette société et les paysages naturels et construits respectivement. Naissance d'un pont semble promouvoir une attitude à l'égard de la nature qui est plus humble que celle dominant dans la société moderne. Afin de pouvoir juger la capacité du roman de transmettre ces valeurs au lecteur, il serait pourtant raisonnable de tenir compte de plusieurs facteurs, tels que la complexité du langage et le niveau de crédibilité de l'histoire.