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<p>At risk of bringing a sprawling and hugely interesting thread somewhat back on track, I agree with Adam Tallman's very first post that singular spectrograms (and pitch tracks, similarly) are sometimes treated with undue veneration in descriptive work. Reviewers
insisting on them may be dictated more by what Gigerenzer has called 'statistical rituals' than by true evidentiary requirements.</p>
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<p><span style="font-size: 12pt;">I agree with others upthread that in this respect, interactional linguistics and conversation analysis</span><span style="font-size: 12pt;"> provide useful models of how to deal with qualitative analysis of such data in ways
that are </span><span style="font-size: 12pt;">more </span><span style="font-size: 12pt;">attentive to the situated production of speech, and perhaps also </span>less epistemologically naďve<span style="font-size: 12pt;">. Work on phonetics and prosody by
</span><span style="font-size: 12pt;">Local & Walker</span><span style="font-size: 12pt;">, Betty Couper-Kuhlen, Richard Ogden and others comes to mind.</span><br>
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<p>While everybody seems to agree that singular examples are at best illustrative (to use Christian Lehman's term), I haven't seen a lot of discussion of another aspect of Adam's original post, namely how to deal with larger amounts of examples. </p>
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<p>Anyone who can generate a single pitch track can also generate multiple, and often this is useful, if only because visualizing one's data allows one to spot outliers (and hopefully make sense of them).<span style="font-size: 12pt;"> For instance, I attach
a figure from Andreas Liesenfeld's work on Cantonese (2019). This provides a useful impression of attested diversity by showing pitch tracks and relative vowel frequencies for 41 tokens.
</span><span style="font-size: 12pt;">I'm using pitch tracks here because that was one of Adam's qualms, bu</span><span style="font-size: 12pt;">t of course sociolinguists have led the way here in the quantitative comparison of </span><span style="font-size: 12pt;">vowel
qualities</span><span style="font-size: 12pt;"> and other sociolinguistic variables.</span></p>
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<p>Acoustic data in general is hard to compare because of its continuous and multidimensional nature. Linguists seem to naturally gravitate towards discrete representations like IPA (as in Mielke 2018). But there are some recent developments in bioacoustics
that I think might also be interesting for linguists and typologists. This work <span style="font-size: 12pt;">makes it possible to do large-scale comparisons of spectrograms and acoustic data using methods of dimensionality reduction (e.g., Sainburg et al.
2020)</span><span style="font-size: 12pt;">. </span></p>
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<p><span style="font-size: 12pt;">We've recently explored the use of these methods on conversational speech data
</span><span style="font-size: 12pt;">for what we call '</span><span style="font-size: 12pt;">b</span><span style="font-size: 12pt;">ottom-up discovery' of structure and variation in response tokens</span><span style="font-size: 12pt;"> </span><span style="font-size: 12pt;">(Liesenfeld
& Dingemanse 2022)</span><span style="font-size: 12pt;">. One useful consequence of this kind of approach is that we don't need to limit ourselves to what can be written down (orthographically, phonemically or phonetically) but can also work with representations
that are closer to the original speech signals. This allows us to capture gradience and variation while at the same time visualizing larger scale patterns and distinctions. </span></p>
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<p><span style="font-size: 12pt;">Needless to say I don't think these kinds of methods (or any) can stand alone; we always benefit from methodological triangulation, and even singular illustrative examples will continue to have their place. But perhaps this
shows at least the possibility and utility of using richer representations of acoustic material in comparative studies.</span></p>
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<p>Refs cited: </p>
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<div>Couper-Kuhlen, E., & Ford, C. E. (2004). Sound patterns in interaction: Cross-linguistic studies of phonetics and prosody for conversation. Amsterdam: John Benjamins.</div>
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<div>Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606. doi: 10.1016/j.socec.2004.09.033</div>
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<div><span style="font-size: 12pt;">Liesenfeld, A. (2019). Cantonese turn-initial minimal particles: Annotation of discourse-interactional functions in dialog corpora. Proceedings of the 33rd Pacific Asia Conference on Language, Information and Computation,
471–479. </span><a href="https://liesenf.github.io/publication/paclic33/" class="OWAAutoLink" style="font-size: 12pt;">https://liesenf.github.io/publication/paclic33/</a><br>
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Liesenfeld, A., & Dingemanse, M. (2022). Bottom-up discovery of structure and variation in response tokens (‘backchannels’) across diverse languages. Proceedings of Interspeech 2022. doi: <a href="https://doi.org/10.21437/Interspeech.2022-11288" class="OWAAutoLink">https://doi.org/10.21437/Interspeech.2022-11288</a> </div>
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<div>Local, J., & Walker, G. (2005). Methodological imperatives for investigating the phonetic organization and phonological structures of spontaneous speech. Phonetica, 62(2–4), 120–130. doi: 10.1159/000090093</div>
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<div>Mielke, J. (2018). Visualizing phonetic segment frequencies with density-equalizing maps. Journal of the International Phonetic Association, 48(2), 129–154. doi: 10.1017/S0025100317000123</div>
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<div>Ogden, R. (2012). Making Sense of Outliers. Phonetica, 69(1–2), 48–67. doi: 10.1159/000343197</div>
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Sainburg, T., Thielk, M., & Gentner, T. Q. (2020). Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires. PLOS Computational Biology, 16(10), e1008228. doi: 10.1371/journal.pcbi.1008228</div>
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<p>Best regards,</p>
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<p>Mark</p>
<p>--</p>
<p>Mark Dingemanse, PhD</p>
<p>Associate Professor, Language & Communication, Radboud University</p>
<p>PI, Elementary Particles of Conversation</p>
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<div><a href="https://markdingemanse.net/elpaco/papers/" class="OWAAutoLink" style="font-size: 12pt;">https://markdingemanse.net/elpaco/papers/</a><span style="font-size: 12pt;"> </span></div>
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