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.

The Secret Aria on Shame : An Analysis of Narrative Structure and Theme in Coetzee's "Diary of a Bad Year"

Coetzee’s last novel Diary of a Bad Year (2007) has an intriguing triple-voiced narrative structure and deals with the grey area of shame. The narrative is divided between a writer, his written contribution to a book called “Strong Opinions”, and his secretary’s thoughts about both the opinions in the manuscript and her employer’s circumstances. This essay explores the relation between form and theme in Diary of a Bad Year; to see in what way these two fundamental elements of the novel intervene and support each other. By doing so the narrative structure is read through Freud’s structural model of personality, whereby each narrator’s voice is related to the notions of the super-ego, the ego and the id. In other words, this essay argues that the specific threefold narrative structure in Diary of a Bad Year, by reflecting the interrelated parts of human identity, helps in creating and developing the theme of shame, which only exists connected to the human psyche. This connection in turn gives special meaning to the entire narratology of the novel.