A dynamic voice user interface system is provided. The dynamic voice user interface system interacts with a user at a first level of formality. The voice user interface system then monitors history of user interaction and adjusts the voice user interface to interact with the user with a second level of formality based on the history of user interaction.

Dynamic interactive voice interface

Apparatus and method for recording data in a speech recognition system and recognizing spoken data corresponding to the recorded data. The apparatus and method responds to entered data by generating a string of phonetic transcriptions from the entered data. The data and generated phonetic transcription string associated therewith is recorded in a vocabulary lexicon of the speech recognition system. The apparatus and method responds to receipt of spoken data by constructing a model of subwords characteristic of the spoken data and compares the constructed subword model with ones of the recorded lexicon vocabulary recorded phonetic transcription strings to recognize the spoken data as the data identified by and associated with a phonetic transcription string matching the constructed subword string.

Automatic speech recognizer

This report provides an overview of the current issues and techniques for the modelling of dialogues using a computer. A dialogue management system can manage a dialogue between two or more agents, be they human or computer. Recently, increasingly complex dialogues are being modelled which allow a range of discourse phenomena including ellipsis and anaphoric reference. Such dialogues are thought to be similar to those between two humans, and accurate modelling of these phenomena leads to "pleasant", i.e. easy to talk to, and natural human-computer dialogues. Dialogue management can be classified into three often overlapping approaches: discourse grammars, plan-based and collaborative approaches. The design of a system often begins by eliciting the language used initially between two humans and later by Wizard of Oz experiments. Special issues relating to dialogue management systems are discussed including recovery strategies from different types of errors and the coding of dialogue in corpora. Lastly, approaches to evaluation are briefly discussed from the qualitative and quantitative viewpoints, recognising the importance and size of this sub-field.

Dialogue management systems: a survey and overview

https://hal.archives-ouvertes.fr/hal-00692190/document

Review: Some background on dialogue management and conversational speech for dialogue systems

Acquiring domain-specific knowledge necessary for creating a dialog system in a new task-oriented domain is a time consuming task that requires domain expertise. This dissertation explores the feasibility of using a machine learning approach to infer the required domain-specific information automatically from in-domain conversations. In order to achieve this goal, two problems need to be addressed: 1) creating a dialog representation that is suitable for representing the required domain-specific information, and 2) developing a machine learning approach that uses this representation to capture information from a corpus of in-domain conversations. In order to solve the first problem, I propose a form-based dialog structure representation incorporating a three-level structure of task, sub-task, and concept. These components are observable in human dialogs. In terms of representation, tasks and subtasks are represented by forms while concepts are slots in a form. The notion of form is generalized as a repository of related pieces of information so that it can be applied to various types of task-oriented domains. Dialog structure analysis and an annotation experiment are used to demonstrate that the form-based representation has all the required properties: sufficiency, generality, and learnability. The proposed representation is applied to six disparate task-oriented domains (air travel planning, bus schedule inquiry, map reading, UAV flight simulation, meeting, and tutoring). While the formbased approach shows some limitations, it is sufficient to model important phenomena in dissimilar types of task-oriented dialogs, and thus has both sufficiency and generality. The annotation experiment shows that the form-based dialog structure representation can be applied reliably by novice annotators which implies that the representation is unambiguous and learnable. For the second problem, inferring the form-based dialog structure representation from a corpus of in-domain conversations, I divide this dialog structure acquisition problem into two sub-problems, concept identification and form identification, to make the problem tractable. In order to identify a set of domain concepts, two unsupervised concept clustering approaches are investigated: statistical-based clustering and knowledge-based clustering. For most statistical-based clustering algorithms, we are able to find automatic stopping criteria that yield close to optimal results. The statistical-based approaches, which utilize word co-occurrence statistics such as mutual information and

/pdf/learning-the-structure-of-task-oriented-conversations-from-37i9mp29xq.pdf

Learning the Structure of Task-Oriented Conversations from the Corpus of In-Domain Dialogs

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Rapid Prototyping of a Dialogue System Using a Generic Dialogue Development Platform

Experience with a dialogue description formalism for realistic applications

The Danish Spoken Language Dialogue Project: a General Overview

Spoken Language Dialogue Systems

Recognition of continuous speech using neural nets and expert system.

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