Transcript of a Siwu conversation featuring continuers

Transcript from a conversation in Siwu (a Kwa language of Ghana) in which Foster and Beatrice talk about house building and discuss why there might be several unfinished compound houses in their hometown in eastern Ghana. The sum of Beatrice’s contributions in this excerpt is a series of mm-like tokens, which brings home one important function of this kind of item: acknowledging the other’s turn while passing the opportunity to take the floor. But the forms are not all the same: they come in multiple variants and appear to be finely adjusted to their sequential environment.

Dingemanse, M. (2023). Interjections. In E. van Lier (Ed.), The Oxford Handbook of Word Classes (pp. 477–491). Oxford University Press. https://doi.org/10.31234/osf.io/ngcrs PDF

Sequential context of continuers

A Candidate continuer forms in 10 unrelated languages, B shown in their natural sequential ecology (annotations as in the original data), C with spectrograms and pitch traces of representative tokens made using the Parselmouth interface to Praat (Jadoul et al., 2018; Boersma & Weenink, 2013).

Dingemanse, M., Liesenfeld, A., & Woensdregt, M. (2022). Convergent cultural evolution of continuers (mmhm). The Evolution of Language: Proceedings of the Joint Conference on Language Evolution (JCoLE), 61–67. PDF

Sampling response tokens

A. Overview of included languages with dataset size in hours and top 3 sequentially identified response tokens as transcribed in the corpus. B. Location of largest speech community. C. Assessing the impact of sparse data on UMAP projections using three samples of Dutch response tokens. A look at the full dataset (a) and random-sampled subsets of decreasing size (b, c) suggests isomorphism across scales and interpretability of clustering solutions as small as 150 tokens.

Liesenfeld, A., & Dingemanse, M. (2022). Bottom-up discovery of structure and variation in response tokens (‘backchannels’) across diverse languages. Proceedings of Interspeech 2022, 1126–1130. doi: 10.21437/Interspeech.2022-11288 PDF

Cultural evolution of continuers

Continuers (frequent standalone utterances like mm-hm that people often use in succession) differ in interesting ways from other elements that are common, like top tokens (the most common words in a corpus) and discontinuers (frequent standalone utterances that people do not produce in successive streaks). A. Length of tokens for continuers, discontinuers and top tokens in 32 languages. B. Frequencies of major sound classes across types. Vowel nuclei occur across types, but continuers stand out for their preferences for nasals. C. Random forest analysis of 118 continuer forms in 32 spoken languages showing the top 10 most predictive phonemes (out of 29 attested).

Dingemanse, M., Liesenfeld, A., & Woensdregt, M. (2022). Convergent cultural evolution of continuers (mmhm). The Evolution of Language: Proceedings of the Joint Conference on Language Evolution (JCoLE), 61–67. PDF

Clustering response tokens

Response tokens like English mhmm, uhuhh, yeah or Catalan mm, , vale are tricky to study in the wild: their phonetic realizations can be quite different from how they are transcribed. Here we use UMAP, a method for dimensionality reduction used in bioacoustics and other fields, to explore the shape of inventories of response tokens in 16 languages. Every point represents a single response token; the closer two points are the more similar they are acoustically. Spectrograms drawn around the rim of the plots provide a direct view of the acoustic structure of tokens and enable quick sanity checks.

Liesenfeld, A., & Dingemanse, M. (2022). Bottom-up discovery of structure and variation in response tokens (‘backchannels’) across diverse languages. Proceedings of Interspeech 2022, 1126–1130. doi: 10.21437/Interspeech.2022-11288 PDF

Continuers and repair initiators

Two-panel figure showing (A) Typical sequential structures for continuers versus
repair initiators. Continuers are recurring items found in alternation with unique turns (a, c). Repair initiators are recurring items found between a unique turn a and its near-copy a’. (B) Prevalence of sequentially identified candidate continuers and repair initiators, demonstrating the potential of using sequential patterns to identify them in language-agnostic ways. Most frequent formats exemplified in 10 languages (9 phyla), from left to right: Akhoe Hai||om, Hausa, Tehuelche, Gutob, Kerinci, Siwu, Mandarin, German, Korean, Dutch.

Another useful feature of this diagram is that it makes it possible to infer a minimum corpus size for spotting interactional resources of interest. For instance, the smallest corpora among the 10 languages for which tokens are exemplified in the figure are Akhoe Hai||om and Hausa, both corpora that make up less than one hour in total. This appears to be a lower bound for identifying phenomena like repair, though continuers are about an order of magnitude more frequent and so can be reliably found even in smaller corpora.

Liesenfeld, A., & Dingemanse, M. (2022). Building and curating conversational corpora for diversity-aware language science and technology. Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), 1178–1192. https://aclanthology.org/2022.lrec-1.126 PDF

Mhmm over time

Even apparently universal patterns (like the use of ‘mhm’ during tellings) can show important cross-cultural differences. A. Continuers (marked ○) are among the most frequent recipient behaviours in both English and Korean, shown here in four 80 second stretches of tellings. B. However, the relative frequency of continuers is about twice as high in Korean based on 100 random samples of 80 second segments in both languages: on average, 21% of turns are continuers in Korean, against 9% of turns in English (measures expressed this way to control for speech rate differences).

Dingemanse, M., & Liesenfeld, A. (2022). From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 5614–5633. doi: 10.18653/v1/2022.acl-long.385 PDF