Greg Kochanski

Comparing dialects using statistical measures of rhythm.

When we say that "music, poetry and language all have rhythms", what is meant by "rhythm"? What accounts for the rhythmic differences between languages or dialects? We do not have a detailed understanding of what constitutes rhythm in language, though evidence suggests that it is related to patterns of vowel length, stess/accent, and the complexity of consonant clusters. Understanding rhythm can also have practical applications: speech rhythm has been studied as an aid in diagnosing depression, predicting suicide risk, and distinguishing depression from Parkinson's disease.

Within the last decade, tools for quantitative measurements of rhythm have begun to appear. So far, these rhythm measures require much careful manual marking of the speech, and they are highly dependent on the choice of words. Mostly, they have been limited to carefully designed laboratory experiments.

These tools are formulae that compare the lengths of vowels, consonants, and nearby sounds. This project will start with those tools, will test and sharpen them, and then will apply them to a linguistic question: to what extent do British English dialects share a common rhythm?

To sharpen our tools, we will systematically test variants of the rhythm measures and see which variants work best. The resulting formula will be our tool for measuring rhythm. Variants will be tested by checking that they have the right properties: that a person should have a reasonably consistent rhythm from hour to hour, that people who speak the same dialect will share broad rhythmic properties and that different dialects and languages can have different rhythms. We do this by building machine classifiers that will try to separate data into groups based on differences in the rhythm measures; the larger the rhythmic differences are, the more successful the classifier will be. If two languages have distinct rhythms, the classifier will be able to correctly name the correct language when given rhythm measures; if dialects overlap, the classifier will be little better than a guess.

So, we feed each variant of the rhythm measures into a classifier: if the combination can accurately and reliably distinguish different languages, good. If not, we look for a better version. This way, we can systematically search for better and better rhythm measures. When this is done, we will have learned something even before we use the tool: we can look at the formulas for the rhythm measures and see what acoustic characteristics are most important. Then, the most important acoustic properties in the formula are presumably also important to humans.

Human language is mostly unscripted conversation. However, to use this kind of data, we need to understand how the rhythm measures depend on the choice of words. They do because rhythm measures depend on the length of vowels, so lengthening a vowel from "bit" to "beet" will change the measured value. Existing work has largely avoided this problem by requiring subjects to read a prepared text, but we plan to put rhythm experiments using unscripted speech onto a sound footing.

To do so, we will correlate changes in rhythm measures with changes in the phonological properties of the text by learning which combinations of speech sounds increase or decrease different rhythm measures. If freed from the need for a script, clinical applications might become easier. From a theoretical point of view, we can also use these improved rhythm measurements to check our understanding of the phonetics of rhythm.

After this preliminary work, we plan to produce the first quantitative survey of the rhythm of British English dialects. Are their rhythms closely related or not? How much closer together are the rhythms of dialects than typical languages? It is an open question, made more interesting by recent work that shows the intonation of British dialects can be very different. This will provide basic data to help our understanding of how dialects evolve and interact with each other.

See the official ESRC website for project RES-062-23-1323 which contains project details.