We do a lot of things downstream with the machine learning tool we build, but not always can a model reasonably say it “understood the assignment” in the sense that the classifier is trained to do exactly what it we are making it do.

Take for example, Yuan and Liberman (2011), who study the realization of word-final ing in American English. This varies between a dorsal variant [ɪŋ] and a coronal variant [ɪn].¹ They refer to this phenomenon using the layman’s term g-dropping; I will use the notation (ing) to refer to all variants. They train Gaussian mixture models on this distinction, then enrich their pronunciation dictionary so that each word can be pronounced with or without g-dropping; it is as if the two variants are homographs. Then, they perform a conventional forced alignment; as a side effect, it determines which of the “homographs” was most likely used. This does seem to work, and is certainly very clever, but strikes me as a mild abuse of the forced alignment technique, since the model was not so much trained to distinguish between the two variants as produce a global joint model over audio and phoneme sequences.

What would an approach to the g-dropping problem that better understood the assignment look like? One possibility would be to run ordinary forced alignment, with an ordinary dictionary, and then extract all instances of (ing). The alignment would, naturally, give us reasonably precise time boundaries for the relevant segments. These could then be submitted to a discriminative classifier (perhaps an LSTM) trained to distinguish the various forms of (ing). In this design, one can accurately say that the two components, aligner and classifier, understand the assignment. I expect that this would work quite a bit better than what Yuan and Liberman did, though that’s just conjecture at present.

Some recent work by my student Angie Waller (published as Waller and Gorman 2020), involved an ensemble of two classifiers, one which more clearly understood the assignment than the other. The task here was to detect reviews of professors which are objectifying, in the sense that they make off-topic, usually-positive, comments about the professors’ appearance. One classifier makes document-level classifications, and cannot be said to really understand the assignment. The other classifier attempts to detect “chunks” of objectifying text; if any such chunks are found, one can label the entire document as objectifying. While neither technique is particularly accurate (at the document level), the errors they make are largely uncorrelated so an ensemble of the two obtains reasonably high precision, allowing us to track trends in hundreds of thousands of professor reviews over the last decade.

Endnotes

1. This doesn’t exhaust the logical possibilities of variation; for instance, for some speakers (including yours truly), there is a variant with a tense vowel followed by the coronal nasal.

References

Waller, A. and Gorman, K. 2020. Detecting objectifying language in online professor  reviews. In Proceedings of the Sixth Workshop on Noisy User-Generated Text, pages 171-180.
Yuan, J. and Liberman, M. 2011. Automatic detection of “g-dropping” in American English using forced alignment. In IEEE Workshop on Automatic Speech Recognition & Understanding, pages 490-493.