With the increasingly widespread use of personal portable devices, it is essential to devise an efficient method for spoken data retrieval for its resource-limited identity. This investigation proposes two efficient feature-based sentence-matching algorithms for speaker-dependent personal spoken sentence retrieval. Such a system can efficiently retrieve database sentences only partially matched to query sentence inputs. The query and database sentences are initially segmented into equal-sized matching units. A matching plane that comprises matching blocks is then created. For each matching block, a local similarity score is then determined from the feature distance. A whole-matching-plane-based accumulation scheme and a column-based row-based accumulation scheme are then designed to determine the global similarity score. The global similarity score of the matching plane reveals the similarity between the query and database sentences. The proposed algorithms are based on the feature-level comparison and do not require acoustical and language models. Experiments on news titles and personal schedules were conducted. The experimental results show that the proposed algorithms can efficiently work on both PC and HP iPAQ H5550 PDA.