[Paper Review] Turning Speech Into Scripts

Manny Rayner, Beth Ann Hockey+1 more|ArXiv.org|Jun 9, 2000

Speech and dialogue systems12 references3 citations

TL;DR

This paper presents a speech-to-script architecture that transforms spoken input into executable scripts using layered linguistic, dialogue, and domain knowledge. By distinguishing between output and meta-output at each processing stage, the system enables robust handling of pronoun resolution, user misconception correction, and script optimization, with a working prototype demonstrated on a Personal Satellite Assistant simulation.

ABSTRACT

We describe an architecture for implementing spoken natural language dialogue interfaces to semi-autonomous systems, in which the central idea is to transform the input speech signal through successive levels of representation corresponding roughly to linguistic knowledge, dialogue knowledge, and domain knowledge. The final representation is an executable program in a simple scripting language equivalent to a subset of Cshell. At each stage of the translation process, an input is transformed into an output, producing as a byproduct a "meta-output" which describes the nature of the transformation performed. We show how consistent use of the output/meta-output distinction permits a simple and perspicuous treatment of apparently diverse topics including resolution of pronouns, correction of user misconceptions, and optimization of scripts. The methods described have been concretely realized in a prototype speech interface to a simulation of the Personal Satellite Assistant.

Motivation & Objective

• To design a scalable architecture for converting spoken language into executable scripts for semi-autonomous systems.
• To address challenges in spoken dialogue understanding, including pronoun resolution and user error correction.
• To integrate linguistic, dialogue, and domain knowledge into a unified transformation pipeline.
• To enable optimization of generated scripts through meta-information about transformations.
• To validate the approach in a real-world simulation of a Personal Satellite Assistant.

Proposed method

• The system processes speech through successive stages: from acoustic input to linguistic representation, then dialogue state, and finally domain-specific script generation.
• Each transformation stage produces both an output (the next-level representation) and a meta-output describing the transformation type and parameters.
• The meta-output enables consistent handling of ambiguity, such as pronoun resolution, by tracking referential context.
• User misconceptions are corrected by analyzing meta-outputs to detect inconsistencies and trigger clarification or correction actions.
• Script optimization is achieved by applying transformation rules to meta-outputs to reduce redundancy or improve efficiency.
• The final output is a Cshell-like script that can be executed in a simulated environment.

Experimental results

Research questions

• RQ1 How can spoken input be systematically transformed into executable scripts using layered knowledge representations?
• RQ2 How can meta-outputs be used to manage ambiguity and errors in spoken dialogue?
• RQ3 What role does meta-information play in enabling consistent resolution of pronouns and user misconceptions?
• RQ4 How can the transformation pipeline support optimization of generated scripts?
• RQ5 Can this architecture be effectively applied to a realistic robotic assistant interface?

Key findings

• The meta-output mechanism enables a uniform treatment of diverse dialogue phenomena, such as pronoun resolution and error correction, without requiring ad hoc rules.
• The system successfully generated executable scripts from natural spoken input in a simulated Personal Satellite Assistant environment.
• The separation of output and meta-output simplifies reasoning about transformation correctness and enables script optimization.
• The architecture demonstrated robustness in handling user errors and misunderstandings through meta-information tracking.
• The approach was validated in a working prototype, showing feasibility for real-time spoken dialogue systems in robotic applications.
• The method supports extensibility and modularity, allowing incremental refinement of linguistic and domain knowledge.