The value of tablets as reading aids for individuals with central visual field loss:an evaluation of eccentric reading with static and scrolling text

Purpose: Technological devices such as smartphones and tablets are widely available and increasingly used as visual aids. This study evaluated the use of a novel app for tablets (MD_evReader) developed as a reading aid for individuals with a central field loss resulting from macular degeneration. The MD_evReader app scrolls text as single lines (similar to a news ticker) and is intended to enhance reading performance using the eccentric viewing technique by both reducing the demands on the eye movement system and minimising the deleterious effects of perceptual crowding. Reading performance with scrolling text was compared with reading static sentences, also presented on a tablet computer. Methods: Twenty-six people with low vision (diagnosis of macular degeneration) read static or dynamic text (scrolled from right to left), presented as a single line at high contrast on a tablet device. Reading error rates and comprehension were recorded for both text formats, and the participant’s subjective experience of reading with the app was assessed using a simple questionnaire. Results: The average reading speed for static and dynamic text was not significantly different and equal to or greater than 85 words per minute. The comprehension scores for both text formats were also similar, equal to approximately 95% correct. However, reading error rates were significantly (p=0.02) less for dynamic text than for static text. The participants’ questionnaire ratings of their reading experience with the MD_evReader were highly positive and indicated a preference for reading with this app compared with their usual method. Conclusions: Our data show that reading performance with scrolling text is at least equal to that achieved with static text and in some respects (reading error rate) is better than static text. Bespoke apps informed by an understanding of the underlying sensorimotor processes involved in a cognitive task such as reading have excellent potential as aids for people with visual impairments.

Across-formant integration and speech intelligibility:effects of acoustic source properties in the presence and absence of a contralateral interferer

The role of source properties in across-formant integration was explored using three-formant (F1+F2+F3) analogues of natural sentences (targets). In experiment 1, F1+F3 were harmonic analogues (H1+H3) generated using a monotonous buzz source and second-order resonators; in experiment 2, F1+F3 were tonal analogues (T1+T3). F2 could take either form (H2 or T2). Target formants were always presented monaurally; the receiving ear was assigned randomly on each trial. In some conditions, only the target was present; in others, a competitor for F2 (F2C) was presented contralaterally. Buzz-excited or tonal competitors were created using the time-reversed frequency and amplitude contours of F2. Listeners must reject F2C to optimize keyword recognition. Whether or not a competitor was present, there was no effect of source mismatch between F1+F3 and F2. The impact of adding F2C was modest when it was tonal but large when it was harmonic, irrespective of whether F2C matched F1+F3. This pattern was maintained when harmonic and tonal counterparts were loudness-matched (experiment 3). Source type and competition, rather than acoustic similarity, governed the phonetic contribution of a formant. Contrary to earlier research using dichotic targets, requiring across-ear integration to optimize intelligibility, H2C was an equally effective informational masker for H2 as for T2.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.