Abstract In this paper several meaningful audio icons of classic arcade games such as Pong, Donkey Kong, Mario World and Pac-Man are analyzed, using the PRAAT software for speech analysis and musical theory. The analysis results are used to describe how these examples of best practice sound design obtain their meaning in the player's perception. Some aspects can be related to the use of tonal hierarchy (e.g. Donkey Kong and Mario World) which is a western culture related aspect of musical meaning. Other aspects are related to universal expressions of meaning such as the theory of misattribution, prosody, vocalization and cross-modal perceptions such as brightness and the uncanny valley hypothesis. Recent studies in the field of cognitive neuroscience support the universal and meaningful potential of all these aspects. The relationship between language related prosody, vocalization and phonology, and music seems to be an especially successful design principle for universally meaningful music icons in game sound design.
Analysis of spontaneous speech is an important tool for clinical linguists to diagnose various types of neurodegenerative disease that affect the language processing areas. Prosody, fluency and voice quality may be affected in individuals with Parkinson's disease (PD, degradation of voice quality, unstable pitch), Alzheimer's disease (AD, monotonic pitch), and the non-fluent type of Primary Progressive Aphasia (PPA-NF, hesitant, non-fluent speech). In this study, the performance of a SVM classifier is evaluated that is trained on acoustic features only. The goal is to distinguish different types of brain damage based on recorded speech. Results show that the classifier can distinguish some dementia types (PPA-NF, AD), but not others (PD).
From the article: "Individuals with dementia often experience a decline in their ability to use language. Language problems have been reported in individuals with dementia caused by Alzheimer’s disease, Parkinson’s disease or degeneration of the fronto-temporal area. Acoustic properties are relatively easy to measure with software, which promises a cost-effective way to analyze larger discourses. We study the usefulness of acoustic features to distinguish the speech of German-speaking controls and patients with dementia caused by (a) Alzheimer’s disease, (b) Parkinson’s disease or (c) PPA/FTD. Previous studies have shown that each of these types affects speech parameters such as prosody, voice quality and fluency (Schulz 2002; Ma, Whitehill, and Cheung 2010; Rusz et al. 2016; Kato et al. 2013; Peintner et al. 2008). Prior work on the characteristics of the speech of individuals with dementia is usually based on samples from clinical tests, such as the Western Aphasia Battery or the Wechsler Logical Memory task. Spontaneous day-to-day speech may be different, because participants may show less of their vocal abilities in casual speech than in specifically designed test scenarios. It is unclear to what extent the previously reported speech characteristics are still detectable in casual conversations by software. The research question in this study is: how useful for classification are acoustic properties measured in spontaneous speech."
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