The connectionist model of speech processing infers that word frequency and position-in-utterance play a major role in the occurrence of speech errors. First, words that are not frequently used are more likely to result in speech errors since they generally receive less activation than frequently occurring words and require more activation to be chosen. Second, speech errors are more likely to occur near the end of utterances since, according to the given-before-new-principle, utterance-final words convey new information that has not yet been activated in the preceding context. The information of word frequency and position-in-utterance is extracted automatically from 382 utterances of a Mandarin speech error corpus and fed to generalized linear mixed models and a decision-tree based classifier. The results show that word frequency and position-in-utterance can predict the occurrence of speech errors with a performance over (but close to) the majority baseline. Therefore, additional information is required to improve the accuracy in the predictions.