Abstract / truncated to 115 words (read the full abstract)

This thesis presents a corpus-based, comparative analysis of error patterns in human and automatic speech recognition (ASR), based on utterances taken from spontaneous, unscripted face-to-face conversations. The utterances reflect specific patterns that are characteristic of this speaking style: They are disfluent through either a pause, a filler particle (FP), a break in the syntax, or a combination of them. Utterances that originally contained FPs were generally easier to recognise for both humans and ASR – regardless of the FP being cut out or left in the presented stimuli – than disfluent utterances without FPs. In the easier utterances, the best ASR system still had an average word error rate (WER) that was about 4.45% higher ... toggle 5 keywords

syntactic disfluencies – automatic speech recognition – human transcription – word error rate analysis – conversational speech

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