Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index

Abstract

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages.

Estimation of voice-onset time in continuous speech using temporal measures

“…For detecting the burst onsets of stops we adopt the solution proposed in our earlier work. 10 In this paper, we address the latter problem of detection of the voice onsets.…”

“…In our earlier work, 10 the very first instant within a stop burst where the feature plosion index (PI) exceeds a threshold was taken to be the representative closure burst transition (CBT) for that stop. This may correspond to the beginning of the prefrication interval.…”

“…This may correspond to the beginning of the prefrication interval. However, for measuring VOT, the location at which the value of the PI measure 10 is maximal within an interval between the CBT and the detected voice onset is taken to be the reference instant for burst onset. The rationale for this is that the values of the PI within the burst-interval represent the strengths of the release and the instant with the maximum value serves as a "better" choice for the burst onset.…”

“…Burst onsets are detected using the algorithm proposed in our previous work 10 (the parameters and thresholds of the algorithm used are those which offer the equal error rate). For every detected stop-burst, the subsequent voice onset is detected as follows:…”

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification

“…Because only the voiced regions are of interest, a voiced/unvoiced (V/UV) classification scheme based on maximum normalized cross correlation is used to retain only the voiced regions, as in Ref. 18. The ILPR is obtained by inverse filtering three successive pitch periods, retaining only the middle period of the output and repeating the process by shifting the analysis frame by one pitch period till the entire voiced speech segment is traversed.…”