We present LIPS (Lexical Isolation Point Software), a tool for accurate lexical isolation point (IP) prediction in recordings of speech. The IP is the point in time in which a word is correctly recognised given the acoustic evidence available to the hearer. The ability to accurately determine lexical IPs is of importance to work in the field of cognitive processing, since it enables the evaluation of competing models of word recognition. IPs are also of importance in the field of neurolinguistics, where the analyses of high-temporal-resolution neuroimaging data require a precise time alignment of the observed brain activity with the linguistic input. LIPS provides an attractive alternative to costly multi-participant perception experiments by automatically computing IPs for arbitrary words. On a test set of words, the LIPS system predicts IPs with a mean difference from the actual IP of within 1ms. The difference from the predicted and actual IP approximate to a normal distribution with a standard deviation of around 80ms (depending on the model used).

Investigating differences in linguistic usage between individuals who have suffered brain injury (hereafter patients) and those who haven’t can yield a number of benefits. It provides a better understanding about the precise way in which impairments affect patients’ language, improves theories of how the brain processes language, and offers heuristics for diagnosing certain types of brain damage based on patients’ speech. One method for investigating usage differences involves the analysis of spontaneous speech. In the work described here we construct a text corpus consisting of transcripts of individuals’ speech produced during two tasks: the Boston-cookie-theft picture description task (Goodglass and Kaplan, 1983) and a spontaneous speech task, which elicits a semi-prompted monologue, and/or free speech. Interviews with patients from 19yrs to 89yrs were transcribed, as were interviews with a comparable number of healthy individuals (20yrs to 89yrs). Structural brain images are available for approximately 30% of participants. This unique data source provides a rich resource for future research in many areas of language impairment and has been constructed to facilitate analysis with natural language processing and corpus linguistics techniques.