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Reliably detecting salient phonetic-acoustic cues plays an important role in speech recognition based on speech landmarks. Once these speech landmarks are located, not only phone recognition can be performed but some other useful information can be derived as well. This paper focuses on the topic of detecting burst onset landmark, an important phonetic characteristic in stops and affricates. The proposed burst onset detector is based on random forest, a learning algorithm renowned for its high accuracy and efficiency in classification. By appending intermediate detection results to MFCCs, the expanded feature can bring benefit to the recognition of stop and affricate consonants in continuous speech.