Towards Understanding Spontaneous Speech: Word Accuracy vs. Concept Accuracy

In this paper we describe an approach to automatic evaluation of both the speech recognition and understanding capabilities of a spoken dialogue system for train time table information. We use word accuracy for recognition and concept accuracy for understanding performance judgement. Both measures are calculated by comparing these modules' output with a correct reference answer. We report evaluation results for a spontaneous speech corpus with about 10000 utterances. We observed a nearly linear relationship between word accuracy and concept accuracy.Comment: 4 pages PS, Latex2e source importing 2 eps figures, uses icslp.cls, caption.sty, psfig.sty; to appear in the Proceedings of the Fourth International Conference on Spoken Language Processing (ICSLP 96

An exploration of the potential of Automatic Speech Recognition to assist and enable receptive communication in higher education

The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking

Overview of the CLEF-2005 cross-language speech retrieval track

The task for the CLEF-2005 cross-language speech retrieval track was to identify topically coherent segments of English interviews in a known-boundary condition. Seven teams participated, performing both monolingual and cross-language searches of ASR transcripts, automatically generated metadata, and manually generated metadata. Results indicate that monolingual search technology is sufficiently accurate to be useful for some purposes (the best mean average precision was 0.18) and cross-language searching yielded results typical of those seen in other applications (with the best systems approximating monolingual mean average precision)

Gesture production and comprehension in children with specific language impairment

Children with specific language impairment (SLI) have difficulties with spoken language. However, some recent research suggests that these impairments reflect underlying cognitive limitations. Studying gesture may inform us clinically and theoretically about the nature of the association between language and cognition. A total of 20 children with SLI and 19 typically developing (TD) peers were assessed on a novel measure of gesture production. Children were also assessed for sentence comprehension errors in a speech-gesture integration task. Children with SLI performed equally to peers on gesture production but performed less well when comprehending integrated speech and gesture. Error patterns revealed a significant group interaction: children with SLI made more gesture-based errors, whilst TD children made semantically based ones. Children with SLI accessed and produced lexically encoded gestures despite having impaired spoken vocabulary and this group also showed stronger associations between gesture and language than TD children. When SLI comprehension breaks down, gesture may be relied on over speech, whilst TD children have a preference for spoken cues. The findings suggest that for children with SLI, gesture scaffolds are still more related to language development than for TD peers who have out-grown earlier reliance on gestures. Future clinical implications may include standardized assessment of symbolic gesture and classroom based gesture support for clinical groups

Main Concepts for Two Picture Description Tasks: An Addition to Richardson and Dalton, 2016

Background: Proposition analysis of the discourse of persons with aphasia (PWAs) has a long history, yielding important advancements in our understanding of communication impairments in this population. Recently, discourse measures have been considered primary outcome measures, and multiple calls have been made for improved psychometric properties of discourse measures. Aims: To advance the use of discourse analysis in PWAs by providing Main Concept Analysis checklists and descriptive statistics for healthy control performance on the analysis for the Cat in the Tree and Refused Umbrella narrative tasks utilized in the AphasiaBank database protocol. Methods & Procedures: Ninety-two control transcripts, stratified into four age groups (20–39 years; 40–59; 60–79; 80+), were downloaded from the AphasiaBank database. Relevant concepts were identified, and those spoken by at least one-third of the control sample were considered to be a main concept (MC). A multilevel coding system was used to determine the accuracy and completeness of the MCs produced by control speakers. Outcomes & Results: MC checklists for two discourse tasks are provided. Descriptive statistics are reported and examined to assist readers with evaluation of the normative data. Conclusions: These checklists provide clinicians and researchers with a tool to reliably assess the discourse of PWAs. They also help address the gap in available psychometric data with which to compare PWAs to healthy controls

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17