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1. Complexity, Naturalness, and Explanatory Power: The Case of Seenku Argument-Head Tone Sandhi

   McPherson, Laura (January 2019, Proceedings of the 36th West Coast Conference on Formal Linguistics)

   When faced with complex phrasal phonological patterns, linguists are faced with a dilemma: since complex phenomena rarely lend themselves to simple analyses, where is the analytical complexity best justified? This talk explores the question using the test case of argument-head tone sandhi in Seenku (Western Mande, Burkina Faso), arguing that a morphological approach with a hierarchical lexicon offers a fuller account of the data than a complex phonological one. In Seenku, internal arguments trigger sandhi on their following heads. Like Taiwanese, tone changes are largely paradigmatic, but unlike most Sinitic sandhi systems, each base tone has more than one sandhi tone, depending on the argument's tone and whether it is pronominal or non-pronominal. A phonological account would necessitate mechanisms like anti-faithfulness or contrast preservation, but just a single underlying form could be maintained. A morphological account treats the alternations as allomorph selection, which requires a hierarchical lexicon with paradigms of subcategorization frames. Both approaches introduce complexity, but the phonological approach fails to account for several data patterns, including differences between pronominal and non-pronominal arguments, the immutability of multi-tonal heads, and lexical exceptions. Further, the single underlying form would be necessarily abstract, since certain heads appear obligatorily with an argument and hence always undergo sandhi. The allomorph selection approach addresses each of these complications and more naturally characterizes the linguistic competence of Seenku speakers. This result suggests that the lexicon may play a more powerful role than is often assumed, especially in cases where sound change has obscured once transparent phonological motivations. 

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2. Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets

   https://doi.org/10.1145/3593013.3594114  

   Barrett, Teanna; Chen, Quanze; Zhang, Amy (June 2023, ACM Conference on Fairness, Accountability, and Transparency)

   To investigate the well-observed racial disparities in computer vision systems that analyze images of humans, researchers have turned to skin tone as a more objective annotation than race metadata for fairness performance evaluations. However, the current state of skin tone annotation procedures is highly varied. For instance, researchers use a range of untested scales and skin tone categories, have unclear annotation procedures, and provide inadequate analyses of uncertainty. In addition, little attention is paid to the positionality of the humans involved in the annotation process—both designers and annotators alike—and the historical and sociological context of skin tone in the United States. Our work is the first to investigate the skin tone annotation process as a sociotechnical project. We surveyed recent skin tone annotation procedures and conducted annotation experiments to examine how subjective understandings of skin tone are embedded in skin tone annotation procedures. Our systematic literature review revealed the uninterrogated association between skin tone and race and the limited effort to analyze annotator uncertainty in current procedures for skin tone annotation in computer vision evaluation. Our experiments demonstrated that design decisions in the annotation procedure such as the order in which the skin tone scale is presented or additional context in the image (i.e., presence of a face) significantly affected the resulting inter-annotator agreement and individual uncertainty of skin tone annotations. We call for greater reflexivity in the design, analysis, and documentation of procedures for evaluation using skin tone. 

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3. Investigating racial and skin tone biases in automated classification of teachers’ activities in classroom videos.

   Drimalla, J; Korban, M; Foster, J (July 2025, Springer Nature Switzerland)

   Previous studies have shown that artificial intelligence can be used to classify instruction-related activities in classroom videos. The automated classi- fication of human activities, however, is vulnerable to biases in which the model performs substantially better or worse for different people groups. Although algo- rithmic bias has been highlighted as an important area for research in artificial intelligence in education, there have been few studies that empirically investigate potential bias in instruction-related activity recognition systems. In this paper, we report on an investigation of potential racial and skin tone biases in the automated classification of teachers’ activities in classroom videos. We examine whether a neural network’s classification of teachers’ activities differs with respect to teacher race and skin tone and whether differently balanced training datasets affect the performance of the neural network. Our results indicate that, under ordinary class- room lighting conditions, the neural network performs equally well regardless of teacher race or skin tone. Furthermore, our results suggest the balance of the training dataset with respect to teacher skin tone and race has a small—but not necessarily positive—effect on the neural network’s performance. Our study, how- ever, also suggests the importance of quality lighting for accurate classification of teacher-related instructional activities for teachers of color. We conclude with a discussion of our mixed findings, the limitations of our study, and potential directions for future research. 

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4. Akan tone encoding across musical modalities

   https://doi.org/10.32473/sal.52.1and2.133067  

   McPherson, Laura; Obiri-Yeboah, Michael (April 2024, Studies in African Linguistics)

   Musical surrogate languages like talking drums remain understudied in the linguistics literature, despite their close connection with the phonetics and phonology of the spoken language. African surrogate languages tend to be based on tone, making them a unique angle for studying a language’s tonal system. This paper looks at the encoding of Akan tone in three instrumental surrogate languages: the atumpan drums, the seperewa harp, and the abɛntia horn trumpet. Each instrument presents different organological constraints that could shape how the tone system is transposed to musical form. Drawing on novel data elicited with musicians in Ghana, we show that all three systems are built on a two-tone foundation mirroring the Akan tone system, but with subtle differences in the treatment of downstep and intonational effects like phrase-final lowering and lax question intonation. 

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5. Heritage Tone and Effects of the Obligatory Contour Principle

   https://doi.org/10.1163/15507076-bja10051  

   Blum, Mirella L (November 2025, Heritage Language Journal)

   Abstract This paper reports on a case study of differences in contextual tonal processes between a mother (native speaker) and adult son (heritage speaker) who speak the Ngok dialect of Dinka. Dinka (Nilotic, South Sudan) is known for its highly complex suprasegmental system. Tone has a high functional load in the language, and one of the primary ways in which varieties of Dinka are differentiated is via the tone systems. I show that lexical tone appears to be less robust for the son, and that when prompted to speak before his parent, the son exhibits less tone sandhi relative to when the parent speaks first. This has implications for underlying specification and subsequent formal analyses and suggests that lexical specification and contextual tonal processes may be processed on different levels. However, inconsistencies in the son’s speech suggest that the overall picture may be more complex than initially anticipated. 

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