Language Log

George Kingsley Zipf is famous for his work on the power-law distribution of word frequencies, which has come to be known as Zipf's Law. And he's also known for the related "Law of Abbreviation", and the hypothesized balance between effort and efficacy.

In his 1945 paper "The repetition of words, time-perspective, and semantic balance", Zipf looks at a different distribution, which is much less famous:

In the present study we shall attempt to show in preliminary outline how the rate of repetition of words in the stream of speech may be useful not only in indicating what we shall presently define as "time-perspective" but also in elucidating what we shall presently refer to as "semantic balance" – two terms of potential significance in the understanding of personality variants.

"Personality variants?" Wait for it…

According to Wikipedia, word salad

is a "confused or unintelligible mixture of seemingly random words and phrases", most often used to describe a symptom of a neurological or mental disorder. The name schizophasia is used in particular to describe the confused language that may be evident in schizophrenia. The words may or may not be grammatically correct, but they are semantically confused to the point that the listener cannot extract any meaning from them. The term is often used in psychiatry as well as in theoretical linguistics to describe a type of grammatical acceptability judgement by native speakers, and in computer programming to describe textual randomization.

The phrase {word salad} has become increasingly common recently in the popular press, most often as an insulting description of Donald Trump's spontaneous speech. See for example Sahil Kapur and Peter Nicholas, "'Incoherent word salad': Trump stumbles when asked how he'd tackle child care", NBC News 9/6/2024.

Laurent Mottron, "A radical change in our autism research strategy is needed: Back to prototypes", Autism Research 6/2/2021:

ABSTRACT: The evolution of autism diagnosis, from its discovery to its current delineation using standardized instruments, has been paralleled by a steady increase in its prevalence and heterogeneity. In clinical settings, the diagnosis of autism is now too vague to specify the type of support required by the concerned individuals. In research, the inclusion of individuals categorically defined by over-inclusive, polythetic criteria in autism cohorts results in a population whose heterogeneity runs contrary to the advancement of scientific progress. Investigating individuals sharing only a trivial resemblance produces a large-scale type-2 error (not finding differences between autistic and dominant population) rather than detecting mechanistic differences to explain their phenotypic divergences. The dimensional approach of autism proposed to cure the disease of its categorical diagnosis is plagued by the arbitrariness of the dimensions under study. Here, we argue that an emphasis on the reliability rather than specificity of diagnostic criteria and the misuse of diagnostic instruments, which ignore the recognition of a prototype, leads to confound autism with the entire range of neurodevelopmental conditions and personality variants. We propose centering research on cohorts in which individuals are selected based on their expert judged prototypicality to advance the theoretical and practical pervasive issues pertaining to autism diagnostic thresholds. Reversing the current research strategy by giving more weight to specificity than reliability should increase our ability to discover the mechanisms of autism.

A colleague pointed me to Terje Holmlund et al., "Applying speech technologies to assess verbal memory in patients with serious mental illness", NPJ digital medicine 2020:

Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73–0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.

This is great work, but over-interpretation of such results is likely to be a problem. At this stage in the development of the technologies, experimenting with with speech-to-text in such applications is a very good idea, but relying on it without accurate human-corrected transcripts is a very bad idea.