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SIG-AI Fall 2003. Spoken Dialogue Systems. By: Sachin Kamboj. Outline. Introduction to Spoken Dialogue Systems (SDS) Applications of SDS Components of SDS Classification of SDS On the basis of dialogue control On the basis of initiative On the basis of the verification strategy

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  • Introduction to Spoken Dialogue Systems (SDS)
  • Applications of SDS
  • Components of SDS
  • Classification of SDS
    • On the basis of dialogue control
    • On the basis of initiative
    • On the basis of the verification strategy
  • Dialogue Manager Components
  • Challenges in the Design of an SDS
  • Any computer system that interacts with a human using natural language.
  • Computer systems with which humans interact on a turn-by-turn basis and in which spoken natural language plays an important part in the communication. [Fraser 1997]
  • Spoken Dialogue Systems provide an interface between the user and a computer-based application that permits spoken interaction with the application in a relatively natural manner. [McTear 2002]
  • Automated reservation systems
    • CU Communicator System
    • TOOT
    • Mercury Flight Reservation System
  • NL email interfaces
    • ELVIS (EmaiL Voice Interactive System)
    • MailSec
  • Planning & Problem Solving Systems
    • Circuit-Fix-It Shop System
  • Virtual Immersive Worlds (Steve)
  • Automated Banking Systems (Naunce)
  • Multimodal Information Systems (MATCH)

Speech Recognizer





Response Generator

Dialogue Manager

Domain Specific


speech recognition
Speech Recognition
  • Involves the conversion of Spoken Sounds (user utterances) to Text (a string of words)
  • Requires knowledge of Phonetics and Phonology
  • Basic Idea:

Ŵ = argmaxwP(O/W) P(W)

  • Challenges:
    • Variability in speech signal due to the language, speaker and channel.
    • Handling continuous spontaneous speech.
    • Handling large vocabularies.
    • Providing a Speaker Independent Recognition System
language understanding
Language Understanding
  • Converts a sequence of words into a Semantic Representation that can be used by the Dialogue Manager.
  • Involves the use of Morphology, Syntax and Semantics.
  • Example:

I want to fly to California

want(speaker, fly(_x, California))

  • Need robust parsing mechanisms to account for errors in speech recognition and ungrammatical utterances.
dialogue manager
Dialogue Manager
  • “Manages” all the aspects of the dialogue.
    • It takes a semantic representation of the user’s utterance, figures out how the utterance fits in the overall context and creates a semantic representation of the systems response.
  • Performs all of the following:
    • Interprets the user's utterance within the current context.
    • Deal with malformed or unrecognized utterances.
    • Create a user model.
    • Perform grounding so that the user and the system have a common set of beliefs.
    • Manage initiative and system responses.
    • Handle issues of pragmatics in generation.
response generation
Response Generation
  • Involves constructing the message that is to be spoken to the user.
  • Requires the making of decision regarding:
    • What information should be included.
    • How the information should be structured.
    • The form of the message
      • The choice of words
      • The syntactic structure
  • Current systems use simple methods such as the insertion of retrieved data into predefined slots in a template.
speech generation
Speech Generation
  • Translates the message constructed by the response generation component into spoken form.
  • Two approaches may be used:
    • Prerecorded canned speech may be used with spaces to be filled by retrieved or previously recorded samples.
      • You have fifteen new emails.
    • Text-to-speech synthesis
      • Also known as concatenative speech synthesis.
      • Text-to-phoneme conversion. (spēch, d ī’əlộg’)
      • Phoneme-to-speech conversion.
domain specific components
Domain Specific Components
  • The dialogue manager usually needs to interface with some external software such as a database or an expert system.
  • The query or plans thus have to be converted from the internal representation used by the dialogue manager to the format used by the external domain specific system (e.g. SQL or STRIPS style goals).
  • This interfacing is handled by the domain specific components.
classification of sds
Classification of SDS
  • Based on the method used to control the dialogue with the user:
    • Finite state (or graph) based systems
    • Frame based systems
    • Agent based systems
  • Type of initiative
    • User Initiative
    • System Initiative
    • Mixed Initiative
  • Type of verification
    • Explicit Verification
    • Implicit Verification
finite state based systems
Finite State Based Systems
  • The user is taken through a dialogue consisting of a sequence of predetermined steps or stages.
  • The dialogue flow is specified as a set of dialogue states with transitions denoting various alternative paths through the dialogue graph.

System: What is your destination?

User: London

System: Was that London?

User: Yes

System: What day do you want to travel?

User: Friday

System: Was that Sunday?

User: No

System: What day do you want to travel?



Get Travel





Travel Day

finite state based system 2
Finite State Based System (2)
  • Advantages:
    • Simple to construct
    • The required vocabulary and grammar for each state can be specified in advance
      • Results in more constrained speech recognition and language understanding.
  • Disadvantages:
    • Inhibits the user’s ability to ask questions and take initiative.
    • Do not allow over-informative answers.
    • Dialogues are not actually natural.
  • Example: Nuance demo banking system.
frame based system

System: What is your destination?

User: London

System: What day do you want to travel?

User: Friday

System: What is your destination?

User: London on Friday, October 10 around 9 in the morning.

System: I have the following connection…

Frame Based System
  • User is asked questions that enable the system to fill slots in a template in order to perform tasks.
  • Dialogue flow is not predetermined but depends on:
    • the contents of the user’s input
    • the information that the system has to elicit.

Destination City: London

Departure Day: Friday

DepartureDate: October 10

DepartureTime: 09 am

frame based systems 2
Frame Based Systems (2)
  • Act like rule-based systems, taking a particular action based on the current state of affairs.
    • Questions and other prompts that the system can ask should be listed along with conditions that have to be true for that particular question.
  • Advantages:
    • User can provide over-informative answers.
    • Allows more natural dialogues.
  • Disadvantages:
    • Cannot handle complex dialogues.
    • Range of applications limited to systems that elicit information from users and act on the basis of the same.
  • Example: Philips train timetable information system
agent based systems

User: I’m looking for a job in the Calais area. Are there any server?

System: No, there aren’t any employment servers for Calais. However, there is an employment server for Pas-de-Calais and an employment server for Lille. Are you interested in one of these?

User: What time does the bank open?

System: 9 am but they only accept job applications at noon.

User: What time does the bank open?

System: 9 am but the guards come around 8.

Agent Based Systems
  • Allow complex communication between the system, the user and the underlying application in order to solve some problem or task.
  • Many variations depending on the application.
agent based systems 2
Agent Based Systems (2)
  • Communication is viewed as interaction between two agents, each of which is capable of reasoning about its own actions and beliefs.
  • The dialogue model takes the preceding context into account
    • The dialogue evolves dynamically as a sequence of related steps that build on top of each other.
  • Advantages:
    • Allow natural dialogue in complex domains.
  • Disadvantage:
    • Such agents are usually very complex.
    • Hard to build.
dialogue manager components
Dialogue Manager Components
  • Dialogue Model: contains information about:
    • Whether the system or the user should take the initiative
    • Whether explicit or implicit confirmation should be used
    • The kind of speech acts that needs to be generated.
  • User Model: contain the systems beliefs about:
    • What the user knows
    • The user's expertise, experience and ability to understand the system's utterances.
  • Knowledge Base: contains information about the world and the domain.
  • Discourse Context: contains the dialogue history and current discourse.
  • Reference Resolver: performs reference resolution and handles ellipsis.
  • Plan Recognizer and Grounding Module:
    • Interprets the user's utterance given the current context
    • Reasons about the user's goals and beliefs.
  • Domain Reasoner/Planner: generates plans to achieve the shared goals.
  • Discourse Manager: manages the flow of information between all of the above modules.
challenges in the design of an sds
Challenges in the Design of an SDS
  • Recovery from errors
  • Understanding pragmatically ill-formed utterances
  • Design of system prompts
  • Reference resolution
  • Understanding inter-sentential ellipsis
  • Plan recognition
  • Detection of conflicts
  • Performing grounding

And many more…

recovery from errors
Recovery From Errors
  • A SDS should be able to detect errors or misunderstandings and recover from them.
  • Errors may be of the following types:
    • Uncertainties – speech recognition o/p has a low confidence score.
    • Inconsistencies – utterance conflicts with domain model/prev utterances
    • Ambiguities – more than one interpretation of a sentence
  • Luperfoy proposes a recovery strategy based on the following four stage algorithm:
    • Detection
    • Diagnosis (Classification of the error)
    • Repair plan selection
    • Interactive plan execution
pragmatically ill formed utterances
Pragmatically Ill-formed Utterances
  • Listeners assume their beliefs of the world match the speaker’s
    • Hence, listeners interpret the utterances with respect to their beliefs
  • However, the speakers views of the world may differ from those of the listener:
    • As a result, the speakers utterance may be syntactically and semantically correct – yet violate the pragmatic rules.
  • Pragmatically Ill-formed utterances are of two types:
    • Extensional failures
      • How many women on the UD wrestling team are CIS majors?
    • Intensional failures
      • Which apartments are for sale?
      • What advanced placement courses did BOB take in high school?
      • What is Dr. Smith’s home address?
design of system prompts
Design of System Prompts
  • Prompt design is important for:
    • Natural flowing conversations
    • To overcome shortcomings in speech recognition technology
  • One of the most challenging aspects is implicitly letting the user know what they can say. By not knowing:
    • Users can go beyond the functionality of the system
    • Not utilize the system as fully as they could
  • Prompt design is related to initiative

This is AZ Banking. How may I help you?

This is AZ banking. Say ‘check balance’ to check your balance, ‘pay bill’ to pay a bill or ‘transfer funds’ to transfer funds…

  • Prompts should be more explicit in the case of recognition errors and less explicit as the user shows greater familiarity with the system.
reference resolution
Reference Resolution
  • Reference is the process by which speakers use expressions like he and it to refer to entities salient in the discourse.
  • Reference resolution is the process of determining the referent entity of a referring expression.
  • For example:

John went to Bill’s car dealership to check out an Acura Integra.Helooked at itfor about an hour.

Before hebought it, John checked over the Integra very carefully.

inter sentential ellipsis
Inter-sentential Ellipsis
  • Is the use of a syntactically incomplete sentence fragment, along with the context in which the fragment occurs, to communicate a complete thought and accomplish a speech act.
  • Examples:
    • I want to cash this check. Small bills only please.
    • Speaker 1: Who are the candidates for the consultants?

Speaker 2: Mary Smith, Bob Jones and Ann Doe.

Speaker 1: Tom’s recommendations?

  • Carberry, Sandra: “Plan Recognition in Natural Language Dialogue”, ACL-MIT Press Series on Natural Language Processing, MIT Press, 1990.