AI and global AAC symbol communication

Artificial Intelligence (AI) applications are usually built on large trained data models that can recognize and label images, provide speech output from text, process natural language for translation, and be of assistance to many individuals via the internet. For those who are non-verbal or have complex speech and language difficulties, AI has the potential to offer enhanced access to the wider world of communication that can be personalized to suit user needs. Examples include pictographic symbols to augment or provide an alternative to spoken language. However, when using AI models, data related to the use of freely available symbol sets is scarce. Moreover, the manipulation of the data available is difficult with limited annotation, making semantic and syntactic predictions and classification a challenge in multilingual situations. Harmonization between symbol sets has been hard to achieve; this paper aims to illustrate how AI can be used to improve the situation. The goal is to provide an improved automated mapping system between various symbol sets, with the potential to enhance access to more culturally sensitive multilingual symbols. Ultimately, it is hoped that the results can be used for better context sensitive symbol to text or text to symbol translations for speech generating devices and web content

Time-sensitive language modelling for online term recurrence prediction

We address the problem of online term recurrence prediction: for a stream of terms, at each time point predict what term is going to recur next in the stream given the term occurrence history so far. It has many applications, for example, in Web search and social tagging. In this paper, we propose a time-sensitive language modelling approach to this problem that effectively combines term frequency and term recency information, and describe how this approach can be implemented efficiently by an online learning algorithm. Our experiments on a real-world Web query log dataset show significant improvements over standard language modelling

Speech Driven by Artificial Larynx: Potential Advancement Using Synthetic Pitch Contours

Despite a long history of development, the speech qualities achieved with artificial larynx devices are limited. This paper explores recent advances in prosodic speech processing and technology and assesses their potentials in improving the quality of speech with an artificial larynx – in particular, tone and intonation through pitch variation. Three approaches are discussed: manual pitch control, automatic pitch control and re-synthesized speech

EISH – exercises in studying HCI

This paper reports on the outcomes of the December 2006 CONVIVIO Faculty Forum and the proposed framework and guidelines for design exercises to stimulate creativity, developed at the Forum