Search effectiveness for tasks where the retrieval units are clearly defined documents is generally evaluated using standard measures such as mean average precision (MAP). However, many practical speech search tasks focus on content within large spoken files lacking defined structure. These data must be segmented into smaller units for search which may only partially overlap with relevant material. We introduce two new metrics for the evaluation of search effectiveness for informally structured speech data: mean average segment precision (MASP) which measures retrieval performance in terms of both content segmentation and ranking with respect to relevance; and mean average segment distance-weighted precision (MASDWP) which takes into account the distance between the start of the relevant segment and the retrieved segment. We demonstrate the effectiveness of these new metrics on a retrieval test collection based on the AMI meeting corpus.