An investigation of cross-language differences in pitch range for speakers of English and German
Ineke Mennen*, Felix Schaeffler#, & Gerard Docherty^
*ESRC Centre for Bilingualism, Bangor University, UK; #Speech Science Research Centre, Queen Margaret University, UK; ^School of Education, Communication & Language Sciences, Newcastle University, UK.
[email protected]; [email protected]; [email protected]
How to measure pitch range?
- There has been very little focus on pitch range as a source of cross- language differences
- Yet, there is some evidence that when groups of speakers of different languages are compared there can be a significant difference in aspects of pitch range - notwithstanding some degree of overlap
- Two languages where this may well be the case are Southern Standard British English (SSBE) and Northern Standard German (NSG). Anecdotal evidence suggests that people perceive differences between the two languages, with SSBE sounding higher and having more variation
- Pitch range is notoriously difficult to quantify, and many different measures have been reported
- Pitch range can vary along two dimensions, level and span (cf. ):
- Level = the typical pitch height (register) of a speaker’s voice
- Span = the range of frequencies covered by a speaker
- Commonly used measures are long-term distributional (LTD), such as mean or median f0 for level, and maximum minus minimum F0, 90% range, 80% range, or 4 standard deviations around the mean for span
- Patterson  suggests that there are some problems with LTD measures (e.g. pitch tracking errors, non-normal distribution of F0, less perceptual validity)
- Alternative to LTD measures are ‘linguistic’ measures, linked to specific linguistically-defined landmarks in the F0 contour (, based on [2, 3])
- There are two primary dimensions to our investigation:
- To evaluate which measures are best suited to capture any cross-language differences in pitch range
- To use a range of these measures to attempt to identify the nature of the cross-language differences in performance which underpin the differences that people perceive
Method and Participants
- Material and participants:
- First sentence of a short story read by 60 (30 per language) female speakers, between 20 and 40 years of age
- As this material contained too few instances of linguistic tones, these were also derived from a larger part of the story (5 sentences), for a subset of 50 speakers
- LTD measures were derived from raw F0 time series, corrected for tracking errors:
- Level: mean, median, maximum, minimum, quantile (95%, 90%, 75%, 25%, 10%, 5%)
- Span: 100% span (max-min), 90% span, 80% span, 50% span, standard deviation (SD), 4 SD around the mean
- Linguistic measures were derived from linguistically relevant F0 landmarks:
- Level: H*i, Hi, H*, H, L*, L, I, FL
- Span: H*i-FL, H*i-L, H*-FL, H*-L
- All span measures were measured in Hz and semitones (ST), except SD (only Hz)
- Mann-Whitney U tests were used for statistical testing, due to indications for non-normal distribution of some variables
- Stimuli were resynthesised with the ’humming function’ of Praat
- This maintains the pitch contour and the voiced/voiceless distinction, but removes all lexical information
- In order to maintain intensity relationships of the original utterances, the intensity contour of the resynthesised stimuli was multiplied with the original contour of the utterance
- Material, participants and procedure:
- On-going study, aiming at a number of 60 participants (30 per language)
- Currently analysed: 23 German participants
- Participants listen to a delexicalized version of the sentence from the production study (60 stimuli)
- The sentences were taken from the story readings from the production study
- Participants decided whether the stimulus was of English or German origin (binary forced choice), and how confident they were about their judgement (5 pt scale)
- Spearman’s rho was used for individual correlations, due to non-normal distribution of some variables
- Analysed Variable: PEJ
- Tool: Praat
- Original pitch contour was approximated by resynthesized contour, based on pitch targets, changes in slope and interpolation between targets
- Pitch targets were local maxima and minima of the contour
- Changes in slope were also marked where necessary, but not included in present analysis
- Local maxima and minima were labelled H* and L*, if aligned with prominent syllable, and H and L otherwise
- Initial and final targets were labelled separately. Final lows as FL, and the first peak of a phrase was separately marked as H*i or Hi
Percentage of English Judgments per stimulus: How often was a certain stimulus judged as being English?
- LTD Measures
- Level (Figure 1):
- Similar values for mean and median across languages
- Maximum higher for SSBE (but ns)
- 95%, 90% and 75% quantiles were significantly higher in SSBE
- Lower quantiles (25%, 10%, 5%) were not significant.
- Span (examples in Figure 2):
- Generally wider span for SSBE (all measured spans except max – min were significant in Hz and ST, max – min was only significant in ST)
- Linguistic Measures
- Level (larger data-set, Figure 3):
- Significantly higher values in SSBE for H*i and phrase-initial tones (I).
- Final lows (FL) were virtually equal across languages.
- Interestingly, non-initial H* are higher for NSG.
- Span (larger data-set)
- Significantly wider for SSBE in the case of H*i – FL and H*i – L, and significantly wider for NSG in the case of H*-FL and H*-L
- A range of acoustic variables show high correlations with PEJ (see Table 1 opposite)
- Span measures do not exceed the correlations for level measures
- Linguistic measures do not exceed the correlations for LTD measures
 Patterson, D., 2000. A linguistic approach to pitch range modelling. PhD dissertation, University of Edinburgh.
 Ladd, D.R.; Terken, J., 1995; Modelling intra- and inter-speaker pitch range variation. Proceedings of ICPhS. Stockholm, 386-389.
 Shriberg, E.; Ladd, D.R.; Terken, J.; Stolcke, A., 1996. Modeling pitch range variation within and across speakers: predicting f0 targets when ”speaking up”. In Proceedings ICSLP, 1–4, Philadelphia, PA, USA.
- Overall, SSBE and NSG show quite different distributions of tonal categories (see Figure 4)
- This distributional difference had consequences for the linguistic measures in the first sentence:
- only two categories (FL and L) showed sufficient numbers for cross-language comparison. Neither was significantly different across languages
- In order to compare initial peaks, H*i and Hi had to be combined into a single category (P1). P1 was significantly higher for SSBE
- Span measures could only be calculated for P1-FL and P1-L. Both differences were significantly wider in SSBE (when measured in Hz and ST)
Discussion & Conclusion
- There is clear evidence for pitch range differences between SSBE and NSG.
- The linguistic measures indicate that this difference is mainly a consequence of higher H*i in SSBE
- The more frequent use of H* accents in SSBE might also contribute to the impression of a wider pitch range in SSBE (despite the fact that non-initial H* are lower in SSBE)
- Measures taken from the lower end of the F0 distribution (minimum, lower quantiles, FL) do not differ greatly across the languages
- The perception study indicates that people are generally sensitive to the aspects of pitch range which differ across the languages, and that variables that are significant in production also play a role in perception in the context of this task
- Overall, there is no indication that linguistic measures are more perceptually valid than LTD measures (contra ). Further work is under way to investigate the extent to which the perceived pitch range difference between SSBE and NSG is a consequence of across-the-board higher amounts of pitch variation in SSBE, or is linked to only a few and rather local events in the F0 contour
This study was funded by the UK Economic & Social Research Council (RES-000-22-1858). We thank Frank Kügler and his colleagues for the collection of the German data.