New Technique to Enhance the Performance of Spoken Dialogue Systems by Means of Implicit Recovery of ASR Errors

This paper proposes a new technique to implicitly correct some ASR errors made by spoken dialogue systems, which is implemented at two levels: statistical and linguistic. The goal of the former level is to employ for the correction knowledge extracted from the analysis of a training corpus comprised of utterances and their corresponding ASR results. The outcome of the analysis is a set of syntactic-semantic models and a set of lexical models, which are optimally selected during the correction. The goal of the correction at the linguistic level is to repair errors not detected during the statistical level which affects the semantics of the sentences. Experiments carried out with a previouslydeveloped spoken dialogue system for the fast food domain indicate that the technique allows enhancing word accuracy, spoken language understanding and task completion by 8.5%, 16.54% and 44.17% absolute, respectively.Ministerio de Ciencia y Tecnología TIN2007-64718 HAD

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Image and interpretation using artificial intelligence to read ancient Roman texts

The ink and stylus tablets discovered at the Roman Fort of Vindolanda are a unique resource for scholars of ancient history. However, the stylus tablets have proved particularly difficult to read. This paper describes a system that assists expert papyrologists in the interpretation of the Vindolanda writing tablets. A model-based approach is taken that relies on models of the written form of characters, and statistical modelling of language, to produce plausible interpretations of the documents. Fusion of the contributions from the language, character, and image feature models is achieved by utilizing the GRAVA agent architecture that uses Minimum Description Length as the basis for information fusion across semantic levels. A system is developed that reads in image data and outputs plausible interpretations of the Vindolanda tablets

Two Kinds of Concept: Implicit and Explicit

In his refreshing and thought-provoking book, Edouard Machery (2009) argues that people possess different kinds of concept. This is probably true and important. Before I get to that, I will briefly disagree on two other points

Cognitive–linguistic deficit and speech intelligibility in chronic progressive multiple sclerosis

Multiple sclerosis (MS) is a disabling neurological disease with varied symptoms, including dysarthria and cognitive and linguistic impairments. Association between dysarthria and cognitive-linguistic deficit has not been explored in clinical MS studies. In MS patients with chronic progressive (CP) MS, the study aimed to investigate the presence and nature of cognitive-linguistic deficit, association between levels of cognitive-linguistic ability and speech intelligibility and of both of these with functional disability and time since onset (TSO) of MS symptoms. The Arizona Battery for Communication Disorders of Dementia (ABCD) (Bayles and Tomoeda 1993), The Assessment of Intelligibility of Dysarthric Speech (AIDS) Sentence Intelligibility Task (Yorkston and Beukelman 1984) and the Modified Barthel Activities of Daily Living Index (MBADLI) (Shah1998) were administered to 24 CP MS participants with dysarthria. 24 non neurologically impaired participants, matched for gender, age and education, formed a control group. For MS participants, linear regression analysis showed a strong association between ABCD and AIDS (Beta = .89, p = 0.005), no association between ABCD and either MBADLI or TSO, a strong association between AIDS and MBADLI (Beta = 0.60, p = .001), and a trend towards association between AIDS and TSO (Beta = -.29, p = 0.08). Correlations between the four included ABCD construct scores and between these and the total ABCD score were significant (r >.60, p .80). The results revealed a strong association between dysarthria, as measured by connected speech intelligibility testing, and cognitive-linguistic deficit, in people with CP type MS. While some of the impairments which are associated with MS, including motor speech disorder, may influence performance on the ABCD, the data support the conclusion that marked cognitive-linguistic deficit is present in CP type MS patients with dysarthria. Deterioration was global, rather than being indicative of a construct specific deficit, and encompassed language, both expression and comprehension. Episodic memory and linguistic expression were especially affected. Speech and language therapists who work with dysarthric patients with CP MS should monitor cognitive-linguistic impairment. Awareness of this may influence assessment, intervention and management, including the information and advice given to patients and their relatives

Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies

An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have shown that the new models improve model performance.Comment: 17 Page Paper. Self-extracting PostScript Fil

Statistical assessment of speech system performance

Methods for the normalization of performance tests results of speech recognition systems are presented. Technological accomplishments in speech recognition systems, as well as planned research activities are described