Previous work on speech-to-speech translation has suffered from problems of brittleness and low quality (rule-based approaches), or from excessive data requirements and linguistic ineciency (analogical or example-based approaches). In this paper, we present a probabilistic approach to analogical speech translation, and describe its integration with linguistic processing. The evaluation results show that this approach results in high-accuracy translations in limited domains.