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Purpose: A major barrier to the wider use of language sample analysis (LSA) is the fact that transcription is very time intensive. Methods that can reduce the required time and effort could help in promoting the use of LSA for clinical practice and research., Method: This article describes an automated pipeline, called Batchalign, that takes raw audio and creates full transcripts in Codes for the Human Analysis of Talk (CHAT) transcription format, complete with utterance- and word-level time alignments and morphosyntactic analysis. The pipeline only requires major human intervention for final checking. It combines a series of existing tools with additional novel reformatting processes. The steps in the pipeline are (a) automatic speech recognition, (b) utterance tokenization, (c) automatic corrections, (d) speaker ID assignment, (e) forced alignment, (f) user adjustments, and (g) automatic morphosyntactic and profiling analyses., Results: For work with recordings from adults with language disorders, six major results were obtained: (a) The word error rate was between 2.4% for controls and 3.4% for patients, (b) utterance tokenization accuracy was at the level reported for speakers without language disorders, (c) word-level diarization accuracy was at 93% for control participants and 83% for participants with language disorders, (d) utterance-level diarization accuracy based on word-level diarization was high, (e) adherence to CHAT format was fully accurate, and (f) human transcriber time was reduced by up to 75%., Conclusion: The pipeline dramatically shortens the time gap between data collection and data analysis and provides an output superior to that typically generated by human transcribers.

Purpose Analysis of connected speech in the field of adult neurogenic communication disorders is essential for research and clinical purposes, yet time and expertise are often cited as limiting factors. The purpose of this project was to create and evaluate an automated program to score and compute the measures from the Quantitative Production Analysis (QPA), an objective and systematic approach for measuring morphological and structural features of connected speech. Method The QPA was used to analyze transcripts of Cinderella stories from 109 individuals with acute-subacute left hemisphere stroke. Regression slopes and residuals were used to compare the results of manual scoring and automated scoring using the newly developed C-QPA command in CLAN, a set of programs for automatic analysis of language samples. Results The C-QPA command produced two spreadsheet outputs: an analysis spreadsheet with scores for each utterance in the language sample, and a summary spreadsheet with 18 score totals from the analysis spreadsheet and an additional 15 measures derived from those totals. Linear regression analysis revealed that 32 of the 33 measures had good agreement; auxiliary complexity index was the one score that did not have good agreement. Conclusions The C-QPA command can be used to perform automated analyses of language transcripts, saving time and training and providing reliable and valid quantification of connected speech. Transcribing in CHAT, the CLAN editor, also streamlined the process of transcript preparation for QPA and allowed for precise linking of media files to language transcripts for temporal analyses.

Ongoing advances in computer technology have opened up a deluge of new datasets for understanding human behavior (Goldstone & Lupyan, 2016). Many of these datasets provide information on the use of written language. However, data on naturally occurring spoken-language conversations are much more difficult to obtain. A major exception to this is the TalkBank system, which provides online multimedia data for 14 types of spoken-language data: language in aphasia, child language, stuttering, child phonology, autism spectrum disorder, bilingualism, Conversation Analysis, classroom discourse, dementia, right hemisphere damage, Danish conversation, second language learning, traumatic brain injury, and daylong recordings in the home. The present report reviews these resources and describes the ways they are being used to further our understanding of human language and communication.

Primary Objective: To investigate whether the degree of participation by people with severe Traumatic Brain Injury (TBI), and the degree of support by their communication partners (CPs) changes in conversation during subacute recovery., Methods and Procedures: Seventeen pairs of participants with TBI and their CPs were video-recorded during a 10 min casual conversation at 3 and 6 months post-injury. Communication behaviors were rated using the adapted Measure of Participation in Conversation (MPC) and the adapted Measure of Support in Conversation (MSC) at both time points and compared., Results: Inferential analyses showed that there was no significant change in the degree of participation in conversation by participants with TBI and the degree of conversation support by their CPs from 3 to 6 months post. Comparison of qualitative field notes revealed that specific conversational behaviors changed over time, including better turn-taking and topic maintenance., Conclusion: Documenting early communication recovery is a complex and challenging endeavor. The lack of change in conversational effectiveness during the sub-acute period using global rating scales highlights the need for social communication tools that are sensitive to communication recovery following severe TBI.

Purpose: This study examined discourse characteristics of individuals with aphasia who scored at or above the 93.8 cutoff on the Aphasia Quotient subtests of the Western Aphasia Battery-Revised (WAB-R; Kertesz, 2007). They were compared with participants without aphasia and those with anomic aphasia., Method: Participants were from the AphasiaBank database and included 28 participants who were not aphasic by WAB-R score (NABW), 92 participants with anomic aphasia, and 177 controls. Cinderella narratives were analyzed using the Computerized Language Analysis programs (MacWhinney, 2000). Outcome measures were words per minute, percent word errors, lexical diversity using the moving average type-token ratio (Covington, 2007b), main concept production, number of utterances, mean length of utterance, and proposition density., Results: Results showed that the NABW group was significantly different from the controls on all measures except MLU and proposition density. These individuals were compared to participants without aphasia and those with anomic aphasia., Conclusion: Individuals with aphasia who score above the WAB-R Aphasia Quotient cutoff demonstrate discourse impairments that warrant both treatment and special attention in the research literature.

Purpose: This study evaluates how proposition density can differentiate between persons with aphasia (PWA) and individuals in a control group, as well as among subtypes of aphasia, on the basis of procedural discourse and personal narratives collected from large samples of participants., Method: Participants were 195 PWA and 168 individuals in a control group from the AphasiaBank database. PWA represented 6 aphasia types on the basis of the Western Aphasia Battery-Revised (Kertesz, 2006). Narrative samples were stroke stories for PWA and illness or injury stories for individuals in the control group. Procedural samples were from the peanut-butter-and-jelly-sandwich task. Language samples were transcribed using Codes for the Human Analysis of Transcripts (MacWhinney, 2000) and analyzed using Computerized Language Analysis (MacWhinney, 2000), which automatically computes proposition density (PD) using rules developed for automatic PD measurement by the Computerized Propositional Idea Density Rater program (Brown, Snodgrass, & Covington, 2007; Covington, 2007)., Results: Participants in the control group scored significantly higher than PWA on both tasks. PD scores were significantly different among the aphasia types for both tasks. Pairwise comparisons for both discourse tasks revealed that PD scores for the Broca's group were significantly lower than those for all groups except Transcortical Motor. No significant quadratic or linear association between PD and severity was found., Conclusion: Proposition density is differentially sensitive to aphasia type and most clearly differentiates individuals with Broca's aphasia from the other groups.

Purpose: The cross-modal picture-word interference task is used to examine contextual effects on spoken-word production. Previous work has documented lexical-phonological interference in children with specific language impairment (SLI) when a related distractor (e.g., bell) occurs prior to a picture to be named (e.g., a bed). In the current study, the authors examined whether interference also arises with nonwords as distractors., Method: In Study 1, children with SLI (N = 20; ages 7;1 [years;months] to 11;0) and age-matched controls named pictures accompanied by (a) phonologically related nonwords, (b) unrelated nonwords, or (c) the word go (baseline). Stimulus asynchrony (SA) varied across blocks with distractors occurring prior to (-300 ms, -100 ms) or after (+100 ms, +300 ms) the pictures. In Study 2, a cross-sectional sample of children (N = 48, 5;3 to 10;9) and adults (N = 16) performed the same task., Results: Child and adult control participants showed phonological priming (not interference) at early and late SAs, whereas children with SLI showed priming only at late SAs. Effect sizes correlated with language skills (Clinical Evaluation of Language Fundamentals-Fourth Edition scores; Semel, Wiig, & Secord, 2003). In the cross-sectional sample, anticipatory priming at SA -300 varied with age, with larger effects in older children., Conclusions: Children with SLI utilize phonological information when it is available just in time for word production but fail to anticipate upcoming stimuli. Poor anticipatory processing may adversely affect language fluency in children with SLI.

To evaluate theoretical proposals regarding the course of child language acquisition, researchers often need to rely on the processing of large numbers of syntactically parsed utterances, both from children and from their parents. Because it is so difficult to do this by hand, there are currently no parsed corpora of child language input data. To automate this process, we developed a system that combined the MOR tagger, a rule-based parser, and statistical disambiguation techniques. The resultant system obtained nearly 80% correct parses for the sentences spoken to children. To achieve this level, we had to construct a particular processing sequence that minimizes problems caused by the coverage/ambiguity tradeoff in parser design. These procedures are particularly appropriate for use with the CHILDES database, an international corpus of transcripts. The data and programs are now freely available over the Internet.