Abstract
The reliable detection of salient acoustic-phonetic cues in speech signal plays an important role in landmark-based speech recognition. Locating speech landmarks not only assists phone recognition, but also helps extraction of phonetic information. This dissertation focuses on the issue of detecting burst onset, which is the most prominent landmark in stop and affricate consonants. The chosen feature representation is the two-dimensional cepstral coefficients (TDCCs) from a spectro-temporal patch, which are able to highlight the closure-burst transitions that indicate the presences of burst onsets. Then the random forest technique, an ensemble of tree-structured classifiers, employs the feature vectors to detect burst onsets in continuous speech. During the random forest construction, we also proposed an asymmetric bootstrap to deal with the problem of imbalanced training data, which may deteriorate performance of a resulting forest. A series of experiments conducted on an English spoken corpus, TIMIT, demonstrate that the proposed detector provides an efficient and accurate means to detect burst onsets. When the detection results are appended to MFCC vectors, the augmented feature vectors enhance the recognition correctness of stop and affricate consonants.Voice onset time (VOT) of a stop consonant is an interval between its burst onset and voicing onset. Among a variety of research topics on VOT, one that has been concerned for years is how to efficiently measure a VOT. Manual annotation is a feasible way, but it becomes a time-consuming task when corpus size is large. The second part of this dissertation proposes an automatic VOT estimate method which combines an HMM-based state-level forced alignment and an RF-based onset detection. The forced alignment roughly locates stop consonants in continuous speech. Then the onset detector searches each aligned stop segment for its subtle locations of burst and voicing onsets to estimate a VOT. The proposed method is able to onset detection can detect the onsets in an efficient and accurate manner with only a small amount of training data. The evaluation data were extracted from TIMIT corpus, which in total comprises 2,344 word-initial and 1,440 word-medial stops. The experimental results showed that, on average, 57%, 83%, 93%, and 96% of the estimates deviate less than 5 ms, 10 ms, 15 ms, and 20 ms from their manually labeled values respectively. The results also revealed the fact that VOTs of word-initial stops are more accurately estimated than those of word-medial stops. In addition to the accuracy of VOT estimates, factors that may influence the estimate accuracy, i.e., articulation place of a stop, voicing status of a stop, and quality of succeeding vowel, were also investigated.