A Secondary Assessment of the Impact of Voice Interface Turn Delays on Driver Attention and Arousal in Field Conditions

Voice interface use has become increasingly popular in vehicles. It is important that these systems divert drivers’ attention from the primary driving task as little as possible, and numerous efforts have been devoted to categorizing demands associated with these systems. Nonetheless, there is still much to be learned about how various implementation characteristics impact attention. This study presents a secondary analysis of the delay time between when users finish giving commands and when the system responds. It considers data collected on 4 different production vehicle voice interfaces and a mounted smartphone in field driving. Collapsing across systems, drivers showed an initial increase in heart rate, skin conductance level, and off-road glance time while waiting for a system to respond; a gradual decrease followed as delays continued. The observed attentional and arousal changes are likely due to an increase in anticipation following a speech command, followed by a general disengagement from the interface as delay times increase. Safety concerns associated with extended delay times and suggestion of an optimal range for system response times are highlighted

The significance of silence. Long gaps attenuate the preference for ‘yes’ responses in conversation.

In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay – conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load ‘yes’ and ‘no’ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ‘no’ evokes an N400-effect relative to a fast ‘yes’, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive – but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ‘no’ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ‘no’ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response

Aneesah: a novel methodology and algorithms for sustained dialogues and query refinement in natural language interfaces to databases

This thesis presents the research undertaken to develop a novel approach towards the development of a text-based Conversational Natural Language Interface to Databases, known as ANEESAH. Natural Language Interfaces to Databases (NLIDBs) are computer applications, which replace the requirement for an end user to commission a skilled programmer to query a database by using natural language. The aim of the proposed research is to investigate the use of a Natural Language Interface to Database (NLIDB) capable of conversing with users to automate the query formulation process for database information retrieval. Historical challenges and limitations have prevented the wider use of NLIDB applications in real-life environments. The challenges relevant to the scope of proposed research include the absence of flexible conversation between NLIDB applications and users, automated database query building from multiple dialogues and flexibility to sustain dialogues for information refinement. The areas of research explored include; NLIDBs, conversational agents (CAs), natural language processing (NLP) techniques, artificial intelligence (AI), knowledge engineering, and relational databases. Current NLIDBs do not have conversational abilities to sustain dialogues, especially with regards to information required for dynamic query formulation. A novel approach, ANEESAH is introduced to deal with these challenges. ANEESAH was developed to allow users to communicate using natural language to retrieve information from a relational database. ANEESAH can interact with the users conversationally and sustain dialogues to automate the query formulation and information refinement process. The research and development of ANEESAH steered the engineering of several novel NLIDB components such as a CA implemented NLIDB framework, a rule-based CA that combines pattern matching and sentence similarity techniques, algorithms to engage users in conversation and support sustained dialogues for information refinement. Additional components of the proposed framework include a novel SQL query engine for the dynamic formulation of queries to extract database information and perform querying the query operations to support the information refinement. Furthermore, a generic evaluation methodology combining subjective and objective measures was introduced to evaluate the implemented conversational NLIDB framework. Empirical end user evaluation was also used to validate the components of the implemented framework. The evaluation results demonstrated ANEESAH produced the desired database information for users over a set of test scenarios. The evaluation results also revealed that the proposed framework components can overcome the challenges of sustaining dialogues, information refinement and querying the query operations

Methodology and algorithms for Urdu language processing in a conversational agent

This thesis presents the research and development of a novel text based goal-orientated conversational agent (CA) for the Urdu language called UMAIR (Urdu Machine for Artificially Intelligent Recourse). A CA is a computer program that emulates a human in order to facilitate a conversation with the user. The aim is investigate the Urdu language and its lexical and grammatical features in order to, design a novel engine to handle the language unique features of Urdu. The weakness in current Conversational Agent (CA) engines is that they are not suited to be implemented in other languages which have grammar rules and structure totally different to English. From a historical perspective CA’s including the design of scripting engines, scripting methodologies, resources and implementation procedures have been implemented for the most part in English and other Western languages (i.e. German and Spanish). The development of an Urdu conversational agent has required the research and development of new CA framework which incorporates methodologies and components in order overcome the language unique features of Urdu such as free word order, inconsistent use of space, diacritical marks and spelling. The new CA framework was utilised to implement UMAIR. UMAIR is a customer service agent for National Database and Registration Authority (NADRA) designed to answer user queries related to ID card and Passport applications. UMAIR is able to answer user queries related to the domain through discourse with the user by leading the conversation using questions and offering appropriate advice with the intention of leading the discourse to a pre-determined goal. The research and development of UMAIR led to the creation of several novel CA components, namely a new rule based Urdu CA engine which combines pattern matching and sentence/string similarity techniques along with new algorithms to process user utterances. Furthermore, a CA evaluation framework has been researched and tested which addresses the gap in research to develop the evaluation of natural language systems in general. Empirical end user evaluation has validated the new algorithms and components implemented in UMAIR. The results show that UMAIR is effective as an Urdu CA, with the majority of conversations leading to the goal of the conversation. Moreover the results also revealed that the components of the framework work well to mitigate the challenges of free word order and inconsistent word segmentation