Purpose: This study examines the effect of age on language use with an automated analysis of digitized speech obtained from semistructured, narrative speech samples. Method: We examined the Cookie Theft picture descriptions produced by 37 older and 76 young healthy participants. Using modern natural language processing and automatic speech recognition tools, we automatically annotated part-of-speech categories of all tokens, calculated the number of tense-inflected verbs, mean length of clause, and vocabulary diversity, and we rated nouns and verbs for five lexical features: word frequency, familiarity, concreteness, age of acquisition, and semantic ambiguity. We also segmented the speech signals into speech and silence and calculated acoustic features, such as total speech time, mean speech and pause segment durations, and pitch values. Results: Older speakers produced significantly more fillers, pronouns, and verbs and fewer conjunctions, determiners, nouns, and prepositions than young participants. Older speakers' nouns and verbs were more familiar, more frequent (verbs only), and less ambiguous compared to those of young speakers. Older speakers produced shorter clauses with a lower vocabulary diversity than young participants. They also produced shorter speech segments and longer pauses with increased total speech time and total number of words. Lastly, we observed an interaction of age and sex in pitch ranges. Conclusions: Our results suggest that older speakers' lexical content is less diverse, and these speakers produce shorter clauses than young participants in monologic, narrative speech. Our findings show that lexical and acoustic characteristics of semistructured speech samples can be examined with automated methods.