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There is growing evidence that information-theoretic principles influence linguistic structures. Regarding speech several studies have found that phonetic structures lengthen in duration and strengthen in their spectral features when they are difficult to predict from their context, whereas easily predictable phonetic structures are shortened and reduced spectrally. Most of this evidence comes from studies on American English, only some studies have shown similar tendencies in Dutch, Finnish, or Russian. In this context, the Smooth Signal Redundancy hypothesis (Aylett and Turk 2004, Aylett and Turk 2006) emerged claiming that the effect of information-theoretic factors on the segmental structure is moderated through the prosodic structure. In this thesis, we investigate the impact and interaction of information density and prosodic structure on segmental variability in production analyses, mainly based on German read speech, and also listeners' perception of differences in phonetic detail caused by predictability effects. Information density (ID) is defined as contextual predictability or surprisal (S(unit_i) = -log2 P(unit_i|context)) and estimated from language models based on large text corpora. In addition to surprisal, we include word frequency, and prosodic factors, such as primary lexical stress, prosodic boundary, and articulation rate, as predictors of segmental variability in our statistical analysis. As acoustic-phonetic measures, we investigate segment duration and deletion, voice onset time (VOT), vowel dispersion, global spectral characteristics of vowels, dynamic formant measures and voice quality metrics. Vowel dispersion is analyzed in the context of German learners' speech and in a cross-linguistic study. As results, we replicate previous findings of reduced segment duration (and VOT), higher likelihood to delete, and less vowel dispersion for easily predictable segments. Easily predictable German vowels have less formant change in their vowel section length (VSL), F1 slope and velocity, are ...
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