Level discrimination of speech sounds by hearing-impaired individuals with and without hearing amplification

Objectives: The current study was designed to see how hearing-impaired individuals judge level differences between speech sounds with and without hearing amplification. It was hypothesized that hearing aid compression should adversely affect the user's ability to judge level differences. Design: Thirty-eight hearing-impaired participants performed an adaptive tracking procedure to determine their level-discrimination thresholds for different word and sentence tokens, as well as speech-spectrum noise, with and without their hearing aids. Eight normal-hearing participants performed the same task for comparison. Results: Level discrimination for different word and sentence tokens was more difficult than the discrimination of stationary noises. Word level discrimination was significantly more difficult than sentence level discrimination. There were no significant differences, however, between mean performance with and without hearing aids and no correlations between performance and various hearing aid measurements. Conclusions: There is a clear difficulty in judging the level differences between words or sentences relative to differences between broadband noises, but this difficulty was found for both hearing-impaired and normal-hearing individuals and had no relation to hearing aid compression measures. The lack of a clear adverse effect of hearing aid compression on level discrimination is suggested to be due to the low effective compression ratios of currently fit hearing aids

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl