Acquisition of prosody, in addition to vocabulary and grammar, is essential for language learners. However, it has received less attention in instruction. To enable automatic identification and feedback on learners’ prosodic errors, we investigate automatic pitch accent labeling for nonnative speech. We demonstrate that an acoustic-based context model can achieve accuracies over 79% on binary pitch accent recognition when trained on withingroup data.