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The reliable detection of salient acoustic-phonetic cues in speech signal plays an important role in speech recognition based on speech landmarks. Once speech landmarks are located, not only can phone recognition be performed, but other useful information can also be derived. This paper focuses on the detection of burst onset landmarks, which are crucial to the recognition of stop and affricate consonants. The proposed detector is purely based on a random forest technique, which belongs to an ensemble of tree-structured classifiers. By adopting a special asymmetric bootstrapping method, a series of experiments conducted on the TIMIT database demonstrate that the proposed detector is an efficient and accurate method for detecting burst onsets. When the detection results are appended to mel frequency cepstral coefficient vectors, the augmented feature vectors enhance the recognition correctness of hidden Markov models in recognizing stop and affricate consonants in continuous speech.