An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity.

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PARADISE: A Framework for Evaluating Spoken Dialogue Agents

Progress can be measured and encouraged via standards for comparison and evaluation. Though qualitative assessments can be useful in initial stages, quantifiable measures of systems under the same conditions are essential for comparing results and assessing claims. This paper will address the emerging standards for evaluation of spoken language systems.

Evaluation of spoken language systems: the ATIS domain

A prosody analyzer enhances the interpretation of natural language utterances. The analyzer is distributed over a client/server architecture, so that the scope of emotion recognition processing tasks can be allocated on a dynamic basis based on processing resources, channel conditions, client loads etc. The partially processed prosodic data can be sent separately or combined with other speech data from the client device and streamed to a server for a real-time response. Training of the prosody analyzer with real world expected responses improves emotion modeling and the real-time identification of potential features such as emphasis, intent, attitude and semantic meaning in the speaker's utterances.

Emotion detection device and method for use in distributed systems

Apparatus and method are provided for segmenting, parsing, interpreting and formatting the content of instructions such as air traffic control instructions. Output from a speech recognizer is so processed to produce such instructions in a structured format such as for input to other software. There are two main components: an instruction segmenter and a robust parser. In the instruction segmenter, the recognized text produced by the speech recognizer is segmented into independent instructions and each instruction is processed. The instruction segmenter receives a recognized air traffic control or other instruction, and segments it into individual commands. Utterances or other language are thereby broken up into their component instructions by detecting probable instruction boundaries. If normal processing fails, then robust backup processing is invoked, as a fallback after a fixed time per word has elapsed or a processing failure has occurred. The robust parser allows the system to extract information from utterances that are not necessarily well formed or may have extraneous comments in them.

Robust language processor for segmenting and parsing-language containing multiple instructions

A simplification of the process of developing call or dialog flows for use in an Interactive Voice Response system is provided. Three principal aspects of the invention include a task-oriented dialog model (or task model), development tool and a Dialog Manager. The task model is a framework for describing the application-specific information needed to perform the task. The development tool is an object that interprets a user specified task model and outputs information for a spoken dialog system to perform according to the specified task model. The Dialog Manager is a runtime system that uses output from the development tool in carrying out interactive dialogs to perform the task specified according to the task model. The Dialog Manager conducts the dialog using the task model and its built-in knowledge of dialog management. Thus, generic knowledge of how to conduct a dialog is separated from the specific information to be collected in a particular application. It is only necessary for the developer to provide the specific information about the structure of a task, leaving the specifics of dialog management to the Dialog Manager. Computer-readable media are included having stored thereon computer-executable instructions for performing these methods such as specification of the top level task and performance of a dialog sequence for completing the top level task.

Task oriented dialog model and manager

The DARPA Spoken Language community has just completed the first trial evaluation of spontaneous query/response pairs in the Air Travel (ATIS) domain.1 Our goal has been to find a methodology for evaluating correct responses to user queries. To this end, we agreed, for the first trial evaluation, to constrain the problem in several ways:Database Application: Constrain the application to a database query application, to ease the burden of a) constructing the back-end, and b) determining correct responses;

Beyond class A: a proposal for automatic evaluation of discourse

This paper presents the Unisys Spoken Language System, as applied to the Air Travel Planning (ATIS) domain. This domain provides a rich source of interactive dialog, and has been chosen as a common application task for the development and evaluation of spoken language understanding systems. The Unisys approach to developing a spoken language system combines SUMMIT (the MIT speech recognition system [6]), PUNDIT (the Unisys language understanding system [3]) and an Ingres database of air travel information for eleven cities and nine airports (the ATIS database). Access to the database is mediated via a general knowledge-base/database interface (the Intelligent Database Server [4]). To date, we have concentrated on the language understanding and database interface components.

Management and evaluation of interactive dialog in the air travel domain

This paper describes three relatively domain-independent capabilities recently added to the Paramax spoken language understanding system: non-monotonic reasoning, implicit reference resolution, and database query paraphrase. In addition, we discuss the results of the February 1992 ATIS benchmark tests. We describe a variation on the standard evaluation metric which provides a more tightly controlled measure of progress. Finally, we briefly describe an experiment which we have done in extending the n-best speech/language integration architecture to improving OCR accuracy.

Lewis M. Norton

Papers

Robust language processor for segmenting and parsing-language containing multiple instructions

Task oriented dialog model and manager

Beyond class A: a proposal for automatic evaluation of discourse

Management and evaluation of interactive dialog in the air travel domain

Recent improvements and benchmark results for the Paramax ATIS system