A couple of years ago ("Hearing interactions", 2/28/2018), I posted some anecdotal evidence that human perception of speaker change is accurate and usually also pretty fast. I noted that the performance of automatic systems at analogous tasks was distinctly underwhelming in comparison.
The ability to detect talker changes when listening to conversational speech is fundamental to perception and understanding of multitalker speech. In this paper, we propose an experimental paradigm to provide insights on the impact of language familiarity on talker change detection. Two multi-talker speech stimulus sets, one in a language familiar to the listeners (English) and the other unfamiliar (Chinese), are created. A listening test is performed in which listeners indicate the number of talkers in the presented stimuli. Analysis of human performance shows statistically significant results for: (a) lower miss (and a higher false alarm) rate in familiar versus unfamiliar language, and (b) longer response time in familiar versus unfamiliar language. These results signify a link between perception of talker attributes and language proficiency. Subsequently, a machine system is designed to perform the same task. The system makes use of the current state-of-the-art diarization approach with x-vector embeddings. A performance comparison on the same stimulus set indicates that the machine system falls short of human performance by a huge margin, for both languages.
Three decades ago, in 1990, I attended a five-week summer institute on Indo-European linguistics and archeology at the University of Texas (Austin). The institute was organized by Edgar Polomé (1920 [b. Molenbeek-Saint-Jean, Brussels, Belgium]-2000) and Winfred Lehmann (1916 [b. Surprise, Nebraska]-2000) and was supported by a generous grant from the National Endowment for the Humanities. It seemed like half of the most important Indo-Europeanists of the day paraded through the Institute.
I remember Homer Thomas drawing hundreds of pots on the gigantic blackboard, stretching about 50 feet across the front of the lecture hall and 7 or 8 feet tall, and unerringly identifying their site and culture names, pointing out their relationships by shape and ornamentation. That was really quite a breathtaking performance, one that went on a whole week for a couple of hours each day, if I remember correctly.
This little Stück of piecemeal wordplay has been making the rounds on WeChat. It seems to be an amalgam of several little coronavirus memes that had appeared in isolation.
隔离人权没了 With the quarantine, there are no human rights.
不隔离人全没了 Without the quarantine, the humans will be all gone.
天上蝙蝠，地上川普 In the sky are bats, on the earth there's Trump.
一个有毒，一个没谱 One has a virus, the other has no clue/no plan.
不戴口罩你试试 Just try not wearing a face mask.
试试就逝世 If you try it, you'll die.
The whole world is now thoroughly familiar with the name "Wuhan", whereas four months ago, only a small number of people outside of China would have heard of it. Since, two days ago, I posted about Dutch curses, many of which just so happen to be linked to diseases, I am prompted to dust off an old post that is about a colorful curse from Wuhan, which, by the way, is famous among all Chinese cities for the proclivity of its inhabitants to indulge in sharp-tongued imprecations at the slightest provocation. I myself have been witness to their talent in this art, at which the women are especially adept.
Thank you for your great explanation of the reasons behind the famous Kennedy "crisis" misquote. When I was in high school, I had a friend who was Chinese and spoke Mandarin fluently, who explained it to my US History class after the teacher quoted Kennedy. That was over 20 years ago and I remembered that his quote was wrong, but could not remember the explanation I was given well enough to explain it to someone else.
A fuller and more specific version of the title of this post would be "Chinese transcriptions of Indic terms in the translations of An Shigao (Chinese: 安世高; pinyin: Ān Shìgāo; Wade–Giles: An Shih-kao, Korean: An Sego, Japanese: An Seikō, Vietnamese: An Thế Cao) (fl. 148-180 CE) and Lokakṣema (लोकक्षेम, Chinese: 支婁迦讖; pinyin: Zhī Lóujiāchèn) (fl. 147-189)".
With the collaboration of Jan Nattier, Nathan Hill was able to digitize some data from Han Buddhist transcriptions back in 2017 and has now published them as a dataset on Zenodo:
Hill, Nathan, Nattier, Jan, Granger, Kelsey, & Kollmeier, Florian. (2020). Chinese transcriptions of Indic terms in the translations of Ān Shìgāo 安世高 and Lokakṣema 支婁迦讖 [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3757095
In some on-going research on linguistic features relating to clinical diagnosis and tracking, we've been looking at "lexical diversity". It's easy to measure the rate of vocabulary display — you can just use a type-token graph, which shows the count of distinct words ("types") against the count of total words ("tokens"). It's less obvious how to turn such a curve into a single number that can be compared across sources — for a survey of some alternative measures, see e.g. Scott Jarvis, "Short texts, best-fitting curves and new measures of lexical diversity", Language Testing 2002; and for the measure that we've settled on, see Michael Covington and Joe McFall, "Cutting the Gordian knot: The moving-average type–token ratio (MATTR)", Journal of quantitative linguistics 2010. More on that later.
For now, I want to make a point that depends only on type-token graphs. Over time, I've accumulated a small private digital corpus of more than 100 English-language fiction titles, from Tristram Shandy forward to 2019. It's clear that different authors have different characteristic rates of vocabulary display, and for today's post, I want to present the authors in my collection with the highest and lowest characteristic rates.
