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.
Dysarthritic Parkinson speech is characterised by impairment of expressive linguistic prosody, even making it difficult to understand. While rigidity and bradykinesia can be held responsible for a general decline in speaking ability, the origin of prosodic impairment must be seen in the light of the accompanying impairments of receptive prosody such as the inability to recognize intonational meaning and make lexical distinctions based on stress contrasts . The stimulating effect of music on motor coordination in afflicted patients suggests that music might have a similar effect on vocal behavior. It could be hypothesized that the singing of Parkinson patients might remain relatively unaffected by the disease. In this study, vocal improvisation was used to compare the singing of Parkinson patients with that of healthy controls, matched for age and gender. When F0 , range, mean absolute slope, and tempo were contrasted, Parkinson patients did not differ significantly from controls.
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).
