1. Understanding Variation of VOT in spontaneous speech
Yao Yao UC Berkeley

2. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

3. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

4. Background Keywords VOT (Voice Onset Time) VOT Variation
Spontaneous speech
VOT (Voice Onset Time)
The duration of time between consonant release and the beginning of voicing of the next vowel
Sensitive to speaker and speaking environment
close
release
vowel onset
A talk on VOT variation for speaker identification in this conf.

5. Background What conditions length of VOT? Place of articulation (POA)
VOT increases as POA moves backward, i.e. [p]<[t]<[k]
Following vowel
Speaking rate
Age, gender
Dialectal background
Speech disorders
Lung volume
Hormone level …

6. Background Why using spontaneous speech data?
Previous results are mostly based on experimental data or read speech.
The existence of large-scale transcribed speech corpora makes it possible to study patterns with “naturalistic” data. (Cf. Bell et al. 1999, Gahl in press, Raymond et al. 2006, etc)

7. Background Experimental data Spontaneous data Controlled content
Easy to investigate individual factors
Hard to see the general pattern of variation
Not necessarily natural speech
Spontaneous data
Uncontrolled content
Need to statistically control for irrelevant factors
Provides a general picture of variation
More naturalistic. Include factors such as disfluency

8. Background Purpose of this study Main statistical tool
To investigate some of the factors that have been shown to affect VOT in experiments, as well as those that have been proposed to influence spontaneous speech production
Main statistical tool
Linear regression
Adding variables step by step

9. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

10. Data Buckeye corpus (Pitt et al. 2005) 40 speakers
All residents at Columbus, Ohio
Balanced in age and gender
1-hr interview
Transcribed at word and phone level
19 speakers’ transcriptions were available at the time of this study
At the time of this study, 19 speakers’ data were transcribed completely.

11. Data 2 speakers’ data are used for this study Target tokens
F07: Older, female, low speaking rate (4.022 syllables/sec)
M08: Younger, male, high speaking rate (6.434 syllabes/sec)
Target tokens
word-initial transcribed voiceless stops (i.e., [p], [t], [k])

12. Data Finding point of burst
An automatic algorithm is used first. (cf. Yao 2007)
>70% of the tokens are checked manually. Error <3.5 ms.
Some tokens are rejected by the algorithm for not having significant burst point.
3.03% of F07’s tokens are rejected % of M08’s tokens are rejected.
Number of tokens
F07
M08
Target tokens
231
618
Target tokens with burst point found
210
466

13. VOT by speaker F07: Mean = 57.41ms, SD = 26.00ms
M08: Mean = 34.86ms, SD = 19.82ms

14. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

15. Preliminary analysis: POA
VOT by POA in F07
VOT by POA in M08
Canonical rule: VOT increases when POA moves backward, from lips to the palate.
This trend is shown in M08’s data (F(2,463, ) =1.77e-6), but not in F07 (F(2, 207, ) = ).
p t k
p t k

16. Preliminary analysis: Word class
Split the data set into three subsets
Content words
Function words
Other. (e.g. proper names)
Number of words of different classes
Content
Function
Other
F07
155 47 8
M08
346
104 16

17. Preliminary analysis: Word class
VOT by word class in F07
VOT by word class in M08
Previous literature shows that words of different classes are processed differently. In particular, function words are processed differently than content words. (XXX; YYY)
Content words and function words differ greatly in usage frequency. In general, function words are much more often used than content words. Therefore, there could also be a confounding frequency effect.
In both speakers, function words are on average shorter than content words, but the variation is vast.
F07: F(2, 206, ) =
M08: F(2,457, ) =6.541e-07
function content other
function content other

18. Preliminary analysis: word class
Word class distinction or general effect of frequency?
Obviously word frequency and word class are two related measures, since function words are in general much more frequently used than content words.

19. Preliminary analysis: word frequency
Two frequency measures:
Log of Celex frequency
Log of Buckeye frequency (speaker-specific)
The two measures are highly correlated (r=0.826)
Effect: more frequent words have shorter VOT
The effect of word class suggests that there might be a more general effect of word frequency: word forms that are used more often tend to be shorter.
R^2 indicates how much variance is explained in the regression model.
Frequency effect
Celex frequency
Buckeye frequency p
R^2 (%)
F07
<0.001
5.1
4.8
M08
4.9
5.9

20. Word class vs. frequency
After factoring out the effect of word class, frequency is no longer significant in F07’s data (p=0.277), but still in M08’s data (p=0.003)
This suggests that the above frequency effect in F07 is mainly due to the effect of word class. In other words, we need to factor out the effect of word class if we really want to study the effect of frequency.
Previous literature also suggests that content words and function words are processed differently, therefore it’s hard to see homogeneous effect in the overall dataset, that the two must be separated.

21. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

22. Linear regression model
We decide to only model the variation in the content word set
F07: 155 tokens
M08: 346 tokens
Factors investigated
POA
Word frequency
Phonetic context
Speech rate
Utterance position

23. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

24. Regression: POA
The canonical rule of [p] <[t] <[k] is only shown in M08’s data, not in F07’s data.
F07
M08 p
0.216
<0.001
R-squared(%)
9.2
Not significant in F07’s content word set, but still in M08’s content word set.

25. Regression: word frequency
In both speakers’ data, more frequent words tend to have shorter VOT, but the trends are not very significant.
For both speakers, Buckeye frequency measure is slightly better than Celex frequency measure.
Not significant in F07’s content word set, but still in M08’s content word set.

26. Regression: word frequency
M08
Log Celex freq p
R^2 (%)
R^2 change of the model (%)
0.391
0.2
0  1.3
Log Celex freq p
R^2 (%)
R^2 change of the model (%)
0.169
0.3
9.2  9.4
Buckeye freq (speaker-specific) p
R^2 (%)
R^2 change of the model (%)
0.577
0  1.7
Buckeye freq (speaker-specific) p
R^2 (%)
R^2 change of the model (%)
0.067
0.7
9.2  9.6

27. Regression: phonetic context
Two measures
Category of the previous phone
Coded as C(onsonant), V(owel), O(other sound), and N(on-linguistic)
Category of the next phone

28. Regression: Phonetic context
F07
M08
Previous phone category p
R^2 (%)
R^2 change of the model (%)
0.141
0.7
1.7  1.9
Previous phone category p
R^2 (%)
R^2 change of the model (%)
0.127
0.4
9.6  9.27
Next phone category p
R^2 (%)
R^2 change of the model (%)
0.563
0.4
1.7  0.6
Next phone category p
R^2 (%)
R^2 change of the model (%)
0.036
0.9
9.6  10.08

29. Regression: phonetic context
VOT by previous phone category in F07
VOT by next phone category in M08

30. Regression: speech rate
Three speed measures
Duration of the next phone, in ms.
Average speed of a 3-word period centered at the target word, measured in # of syll/s.
Average speed of the pause-bounded stretch that contains the target word, measured in # of syll/s.
All speed measures predict that words in faster speech tend to have shorter VOT

31. Regression: speech rate
F07
M08
Duration of next phone p
R^2 (%)
R^2 change of the model (%)
0.014
3.2
1.9  5.1
Duration of next phone p
R^2 (%)
R^2 change of the model (%)
0.342
 16.62
Average of the 3-wd stretch p
R^2 (%)
R^2 change of the model (%)
<0.001
10.93
1.9  11.8
Average of the 3-wd stretch p
R^2 (%)
R^2 change of the model (%)
<0.001
4.1
 12.85
Average of the local stretch p
R^2 (%)
R^2 change of the model (%)
<0.001 6
1.9  7.1
Average of the local stretch p
R^2 (%)
R^2 change of the model (%)
0.014
1.4
 15.07

32. Regression: utterance position
Utterance-final lengthening has been documented in the literature extensively.
We code tokens for whether they are followed by silence.
Number of tokens
F07
M08
Non-final
146
312
final 9 34

33. Regression: utterance position
F07
M08
non-final final
non-final final

34. Regression: utterance position
F07
M08
Utterance position p
R^2 (%)
R^2 change of the model (%)
0.021
2.8
11.8  19.11
Utterance position contributes to the variation in VOT
Utterance position p
R^2 (%)
R^2 change of the model (%)
0.652
0.2
 13.31
Utterance position doesn’t contribute to the variation in VOT

35. Regression: complete model
F07
M08
Model performance
Variable added
R^2 (%)
POA
Buckeye Frequency
1.7
Previous phone category
1.9
Average speed of the 3-word stretch
11.8
Utterance position
19.11
Model performance
Variable added
R^2 (%)
POA
9.2
Buckeye Frequency
9.6
Next phone category
10.08
Duration of the next phone
16.62

36. Regression: trends observed
POA
[p]<[t]<[k]
Word class
function words < content words
Word frequency
??Higher frequency  shorter VOT
Here the word shorter or longer are used loosely, not referring to the specific effect of lengthening or shortening.

37. Regression: trends observed
Phonetic category
??Preceded by vowel  shorter VOT
??Followed by vowel  longer VOT
Speaking rate
Faster speech  shorter VOT
Utterance position
Utterance final  longer VOT

38. Regression: trends observed
Missing from the picture
Contextual predictability
Stress
Disfluency
Emotion

39. Overview Background Methodology Results Discussion Data
Preliminary analysis
Regression model
Results
Discussion

40. Discussion Individual differences Other between-subject factors
Measurements
Other between-subject factors
Age
Gender
Average speaking rate

41. Discussion
Relatively little variation is explained in the full model. (19.11% in F07 and 16.62% in M08)
Factors missing from the picture: contextual predictability, stress, disfluency, etc.
Limitation of linear regression model
Non-linear effect
Non-homogeneous effect
Mixture of categorical and continuous variables

42. Discussion Echoing and challenging previous findings VOT and POA
Canonical rule is observed in M08, but not in F07
Word frequency effect
Overshadowed by word class distinction
Utterance-final lengthening
Significant in F07, but not M08
Speaking style?
Content words vs. function words?
Speed measures?
Given speed measures, the partial correlation between vot and utterance-position in F07 is
(1) if spd = next_dur, p =
(2) if spd= spd_3wd, p =
(3) if spd= str_spd, p =0.0743

43. Conclusion
Still a long way to go to model VOT variation in spontaneous speech… Thanks! Any comments are welcome!

44. Thanks to Anonymous subjects Contributors to the Buckeye corpus
Prof. Keith Johnson
Members of the phonology lab in UC, Berkeley

45. Selected references
Bell, A. et al. (1999) Forms of English function words - Effects of disfluencies, turn position, age and sex, and predictability. Proceedings of ICPhS-99
Gahl, S. In press. "Time" and "thyme" are not homophones: The effect of lemma frequency on word durations in a corpus of spontaneous speech. To appear in Language.
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
Raymond et al. (2006) Word-internal /t,d/ deletion in spontaneous speech: Modeling the effects of extra-linguistic, lexical, and phonological factors.
Yao, Y. (2007) Closure duration and VOT of word-initial voiceless plosives in English in spontaneous connected speech. UC Berkeley PhonLab report