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Automatic Burst Detection in Voiceless Stops: An Exemplar-based Approach

This study presents an exemplar-based method for detecting burst points in voiceless stops in speech signals. Using spectral measures, spectral templates, and similarity scores, the algorithm identifies the release points of voiceless stops accurately, with minimal error. The methodology involves comparing spectral vectors, assessing fricative and silence likeness, and finding points of maximal slope to determine the release point. Tuning the algorithm involves rejection rules to address problematic cases such as weak or multiple releases. The approach is speaker-specific and requires spectral features and templates for analysis. The study uses the Buckeye corpus data for testing and validation.

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Automatic Burst Detection in Voiceless Stops: An Exemplar-based Approach

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  1. An Exemplar-based Approach to Automatic Burst Detection in Voiceless Stops YAO YAO UC BERKELEY YAOYAO@BERKELEY.EDU http://linguistics.berkeley.edu/~yaoyao JULY 25, 2008

  2. Overview • Background • Data • Methodology • Algorithm • Tuning the model • Testing • Results • General Discussion

  3. Background • Purpose of the study • To find the point of burst in a word initial voiceless stop (i.e. [p], [t], [k]) close release vowel onset • Existing approach • Detecting the point of maximal energy change (cf. Niyogi and Ramesh, 1998; Liu, 1996)

  4. Background • Our approach • Compare the spectrogram of the target token at each point against that of fricatives and silence • Assess how “fricative-like” and “silence-like” the spectrogram is at each time point • Find the point where “fricative-ness” suddenly rises and “silence-ness” suddenly drops  point of burst

  5. Background • Our approach (cont’d) • What do we need? • Spectral features of a given time frame • Spectral templates of fricatives and silence • Specific to speaker and the recording environment • Measure and compare fricative-ness and silence-ness • An algorithm to find the most likely point for release • Advantage • Easy to implement • No worries about change in the environment and individual differences

  6. Data • Buckeye corpus (Pitt, M. et al. 2005) • 40 speakers • All residents of Columbus, Ohio • Balanced in gender and age • One-hour interview • Transcribed at word and phone level • 19 used in the current study • Target tokens • Transcribed word-initial voiceless stops (e.g. [p], [t], [k])

  7. Methodology: spectral measures • Spectral vector • 20ms Hamming window • Mel scale • 1 × 60 array • Spectral template • Speaker-specific, phone-specific • Ignore tokens shorter than average duration of that phone of the speaker • For the remaining tokens • Calculate a spectral vector for the middle 20ms window • Average over the spectral vectors

  8. Methodology: spectral template [a] of F01 [f] of F01 Silence of F01

  9. Methodology: similarity scores • Similarity between spectral vectors x and u • Dx,u = • Sx,u = e-0.005Dx,u • Comparing the given acoustic data against any spectral templates of that speaker • Stepsize = 5ms

  10. Similarity scores Formulae: Dx,t = Sx,t =e-0.005Dx,t Step size = 5ms - [s] score - <sil> score

  11. Methodology: finding the release point • Basic idea • Near the release point • - Fricative similarity score rises • - Silence similarity score drops close release vowel onset Q1: Which fricative to use? Q2: Which period of rise or drop to pick?

  12. Methodology : finding the release point • Slope is a better predictor than absolute score value • The end point of a period with maximal slope •  the release point • Which fricative? • [sh] score is more consistent than other fricatives • [h] • [s] • [sh] • <sil> similarity scores

  13. Methodology : finding the release point Initial [t] in "doing" Initial [k] in “countries” • [h] • [s] • [sh] • <sil> • [h] • [s] • [sh] • <sil>

  14. Methodology : finding the release point • Original algorithm • Find the end point of a period of fastest increase in <sh> score • Find the end point of a period of fastest decrease in <sil> score • Return the middle point of the two end points as the point of release • If either or both end points cannot be found within the duration of the stop, return NULL.

  15. Methodology : finding the release point • Select two speakers’ data to tune the model • Hand-tag the release point for all tokens in the test set. • If the stop doesn’t appear to have a release point on the spectrogram, mark it as a problematic case, and take the end point of the stop as the release point, for calculating error.

  16. Methodology : problematic cases no burst no closure weak and double release(??) • [sh] • <sil>

  17. Methodology : finding the release point 17 • Calculate the difference between hand-tagged release point and the estimated one (i.e. error) for each case. • RMS (Root Mean Square) of error is used to measure the performance of the algorithm.

  18. Methodology : error analysis F07 ( n=231 tokens) M08 (n=261 tokens) Add 5ms to the estimation 14.ms RMS = 7.22ms RMS = 13.11ms 4.85ms real release-estimate real release-estimate

  19. Methodology: tuning the algorithm • 1st Rejection Rule -- A target token will be rejected if the changes in scores are not drastic enough. • E.g. • [sh] • <sil> Insignificant rise  Reject!

  20. Methodology: tuning the algorithm • Applying 1st Rejection Rule • Rejecting 4 cases inF07 • RMS(+5ms) = 4.19ms • Rejecting 28 cases in M08 • covering most of the problematic cases • RMS(+5ms)=9.27ms Error analysis in M08 after 1st rejection rule RMS(+5ms) = 14ms 9.27ms

  21. Methodology : tuning the algorithm Still a problem… • Multiple releases • Each might corresponds to a rise/drop of the scores Initial [k] in “cause” of M08 • [sh] • <sil>

  22. Methodology: tuning the algorithm • 2nd Rejection Rule -- A target token will be dropped If the points found in <sh> and <sil> scores are too far apart. (>20ms) • Partly solves the multiple release problem • The ideal way would to identify all candidate release points, and return the first one.

  23. Methodology: tuning the algorithm • Applying 2nd Rejection Rule • Rejecting 3 cases inF07 • RMS(+5ms) = 3.22ms • Rejecting 20 cases in M08 • Only 2 problematic cases remain • RMS(+5ms) = 3.44ms Error analysis in M08 after 2nd rejection rule RMS(+5ms) = 9.26ms 3.44ms Compare: Optimal error is 2.5ms given the 5ms step size…

  24. F07 M08 Methodology: tuning the algorithm Rejection rate: 3.03% Rejection rate: 15.05%

  25. Methodology: testing the algorithm • Select a random sample of 50 tokens from all speakers • Hand-tag the release point • Use the current algorithm together with two rejection rules to find the estimated release. • Compare the hand-tagged point and the estimated one • 4 rejected by the 1st rule (3 were legitimate) • 3 rejected by the 2nd rule (2 were legitimate) • 43 accepted cases. RMS(error) <5ms

  26. Calculate <silence> score and <sh> score Calculate the slope in <silence> score and <sh> score In a labeled voiceless stop span, (i)find the time point of largest positive slope in <sh> score, and store in p1; (ii)find the time point of smallest negative slope in <silence> score, and store in p2 p1 = null or p2 = null slope (p1)<0.02 and slope (p2)>0.04 |p1–p2|>=0.02 s return (p1+p2)/2+0.005 reject the case Methodology: summary Y N Y N Y N

  27. Results: grand means • Rejection rates (2 rules combined) • Varies from 3.03% to 30.5% (mean = 13.3%,sd= 8.6%) across speakers. • VOT and closure duration

  28. Results: VOT by speaker

  29. General Discussion • Echoing previous findings • Byrd (1993): Closure duration and VOT in read speech • Shattuck-Hufnagel &Veilleux (2007): 13% of missing landmarks in spontaneous speech

  30. General Discussion • Future work • Fine-tune the 2nd rejection rule • Generalize the exemplar-based method for other automatic phonetic processing problem?

  31. Acknowledgement • Anonymous speakers • Buckeye corpus developers • Prof. Keith Johnson • Members of the phonology lab in UC Berkeley Thank you! Any comments are welcome.

  32. References • Byrd, D. (1993) 54,000 American stops. UCLA Working Papers in Phonetics. No 83, pp: 97-116. • Johnson, K. (2006) Acoustic attribute scoring: A preliminary report. • Liu, S. (1996) Landmark detection for distinctive feature-based speech recognition. J. Acoust. Soc. Amer. Vol 100, pp 3417-3430. • Niyogi, P., Ramesh, P. (1998) Incorporating voice onset time to improve letter recognition accuracies. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '98. Vol 1, pp: 13-16. • Pitt, M. et al. (2005) The Buckeye Corpus of conversational speech: labeling conventions and a test of transcriber reliability. Speech Communication. Vol 45, pp: 90-95 • Shattuck-Hufnagel, S., Veilleux, N.M. (2007) Robustness of acoustic landmarks in spontaneously-spoken American English. Proceedings of International Congress of Phonetic Science 2007, Saarbrucken, August 2007. • Zue, V.W. (1976) Acoustic Characteristics of stop consonants: A controlled study. Sc. D. thesis. MIT, Cambridge, MA.

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