intonational variation in spoken dialogue systems

04/19/231

AT&T Labs AT&T Labs ResearchResearch

Intonational Variation in Spoken Intonational Variation in Spoken Dialogue SystemsDialogue Systems

Generation and UnderstandingJulia Hirschberg

Charles UniversityMarch 2001

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Talking to a Machine….and Talking to a Machine….and Getting an AnswerGetting an Answer

• Today’s spoken dialogue systems make it possible to accomplish real tasks, over the phone, without talking to a person Real-time speech technology enables real-time

interaction Speech recognition and understanding is ‘good

enough’ for limited, goal-directed interactions Careful dialogue design can be tailored to

capabilities of component technologies• Limited domain• Judicious use of system initiative vs. mixed initiative

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Some RepresentativeSome RepresentativeSpoken Dialogue SystemsSpoken Dialogue Systems

1980+ 1990+ 1993+ 1995+ 1997+ 1999+

Mixed Initiative

System Initiative

Banking(ANSER)

Deployed

ATIS(DARPA Travel)

MITGalaxy/Jupiter

DirectoryAssistant (BNR)

Multimodal Maps(Trains, Quickset)

Customer Care(HMIHY – AT&T)

Communications(Wildfire, Portico)

Train Schedule(ARISE)

Communicator(DARPA Travel)

Brokerage(Schwab-Nuance)

Air Travel(UA Info-SpeechWorks)

E-MailAccess(myTalk)

User

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But we have a long way to go…But we have a long way to go…

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Course OverviewCourse Overview

• Spoken Dialogue Systems today Evaluating their weaknesses Role of intonational variation

• Importance of corpora and conventions for annotating them

• Intonational ‘meanings’ • Prosody in Speech Generation• Prosody in Speech Recognition/

Understanding

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• Spoken Dialogue Systems today Evaluating their strengths and weaknesses Role of intonational variation



Understanding

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Evaluating Dialogue SystemsEvaluating Dialogue Systems

• PARADISE framework (Walker et al ’00)• “Performance” of a dialogue system is

affected both by what gets accomplished by the user and the dialogue agent and how it gets accomplishedMaximizeMaximize

Task SuccessTask Success Minimize Minimize

CostsCosts

EfficiencyEfficiencyMeasuresMeasures

QualitativeQualitativeMeasuresMeasures

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Task SuccessTask Success

Attribute Attribute ValueValueSelection CriterionSelection Criterion Kim Kim oror Meeting MeetingTimeTime 10:30 a.m.10:30 a.m.PlacePlace 2D5162D516

•Task goals seen as Attribute-Value MatrixELVIS e-mail retrieval taskELVIS e-mail retrieval task (Walker et al ‘97)(Walker et al ‘97)

““Find the Find the timetime and and placeplace of your of your meetingmeeting with with KimKim.”.”

•Task success defined by match between AVM values at end of with “true” values for AVM

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MetricsMetrics

• Efficiency of the Interaction:User Turns, System Turns, Elapsed Time

• Quality of the Interaction: ASR rejections, Time Out Prompts, Help Requests, Barge-Ins, Mean Recognition Score (concept accuracy), Cancellation Requests

• User Satisfaction• Task Success: perceived completion,

information extracted

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Experimental ProceduresExperimental Procedures

• Subjects given specified tasks• Spoken dialogues recorded• Cost factors, states, dialog acts

automatically logged; ASR accuracy,barge-in hand-labeled

• Users specify task solution via web page• Users complete User Satisfaction surveys• Use multiple linear regression to model

User Satisfaction as a function of Task Success and Costs; test for significant predictive factors

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User User SatisfactionSatisfaction::Sum of Many MeasuresSum of Many Measures

• Was Annie easy to understand in this conversation? (TTS Performance)

• In this conversation, did Annie understand what you said? (ASR Performance)

• In this conversation, was it easy to find the message you wanted? (Task Ease)

• Was the pace of interaction with Annie appropriate in this conversation? (Interaction Pace)

• In this conversation, did you know what you could say at each point of the dialog?

(User Expertise)• How often was Annie

sluggish and slow to reply to you in this conversation? (System Response)

• Did Annie work the way you expected her to in this conversation? (Expected Behavior)

• From your current experience with using Annie to get your email, do you think you'd use Annie regularly to access your mail when you are away from your desk? (Future Use)

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Performance Functions from Performance Functions from Three SystemsThree Systems

• ELVIS User Sat.= .21* COMP + .47 * MRS - .15 * ET

• TOOT User Sat.= .35* COMP + .45* MRS - .14*ET

• ANNIE User Sat.= .33*COMP + .25* MRS +.33* Help

COMP: User perception of task completion (task success)

MRS: Mean recognition accuracy (cost) ET: Elapsed time (cost) Help: Help requests (cost)

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Performance ModelPerformance Model

• Perceived task completion and mean recognition score are consistently significant predictors of User Satisfaction

• Performance model useful for system development Making predictions about system modifications Distinguishing ‘good’ dialogues from ‘bad’

dialogues

• But can we also tell on-line when a dialogue is ‘going wrong’

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Understanding

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How to Predict Problems How to Predict Problems ‘On-Line’?‘On-Line’?

• Evidence of system misconceptions reflected in user responses (Krahmer et al ‘99, ‘00) Responses to incorrect verifications

• contain more words (or are empty)

• show marked word order (especially after implicit verifications)

• contain more disconfirmations, more repeated/corrected info

‘No’ after incorrect verifications vs. other ynq’s• has higher boundary tone

• wider pitch range

• longer duration

• longer pauses before and after

• more additional words after it

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• User information state reflected response (Shimojima et al ’99, ‘01) Echoic responses repeat prior information – as

acknowledgment or request for confirmationS1: Then go to Keage station.

S2: Keage.

Experiment:• Identify ‘degree of integration’ and prosodic features

(boundary tone, pitch range, tempo, initial pause)• Perception studies to elicit ‘integration’ effect

Results: fast tempo, little pause and low pitch signal high integration

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Can Prosodic Information Help Can Prosodic Information Help Identify Dialogue System Identify Dialogue System

Problems ‘On Line’?Problems ‘On Line’?

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MotivationMotivation

• Prosody conveys information about: The state of the interaction:

• Is the user having trouble being understood?• Is the user having trouble understanding the system?

What the speaker is trying to convey• Is this a statement or a question?

The structure of the dialogue• Is the user or the system trying to start a new topic?

The emotions of the speaker• Is the speaker getting angry, frustrated?

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Past Research Issues and Past Research Issues and ApplicationsApplications

• How prosodic variation influences ‘meaning’ Focus or contrast

Given/new

• How prosodic variation is related to other linguistic components Syntax

Semantics

• How to model prosodic variation effectively

• Applications: Text-to-Speech

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Current TrendsCurrent Trends

• New description schemes (e.g. ToBI)

• Corpus-based research and machine learning

• Emphasis on evaluation of algorithms and systems (NLE ‘00 special issue)

• Investigation of spontaneous speech phenomena and variation in speaking style

• Applications to CTS, ASR and SDS

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Understanding

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CorporaCorpora

• Public and semi-public databases ATIS, SwitchBoard, Call Home (NIST/DARPA/LDC) TRAINS/TRIPS (U. Rochester) FM Radio (BU)

• Private collections Acquired for speech or dialogue research (e.g.

August, Gustafson & Bell ’00) Meeting, call center, focus group collections Accidentally collected

• The Web Mud/Moo dialogues

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To(nes and)B(reak)I(ndices)To(nes and)B(reak)I(ndices)

• Developed by prosody researchers in four meetings over 1991-94

• Goals: devise common labeling scheme for Standard

American English that is robust and reliable

promote collection of large, prosodically labeled, shareable corpora

• ToBI standards also proposed for Japanese, German, Italian, Spanish, British and Australian English,....

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• Minimal ToBI transcription: recording of speech

f0 contour

ToBI tiers: • orthographic tier: words

• break-index tier: degrees of junction (Price et al ‘89)

• tonal tier: pitch accents, phrase accents, boundary tones (Pierrehumbert ‘80)

• miscellaneous tier: disfluencies, non-speech sounds, etc.

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Sample ToBI Labeling

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• Online training material,available at: http://www.ling.ohio-state.edu/phonetics/ToBI/

• Evaluation Good inter-labeler reliability for expert and

naive labelers: 88% agreement on presence/absence of tonal category, 81% agreement on category label, 91% agreement on break indices to within 1 level (Silverman et al. ‘92,Pitrelli et al ‘94)

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Understanding

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Pitch Accent/Prominence in Pitch Accent/Prominence in ToBIToBI

• Which items are made intonationally prominent and how?

• Accent type: H* simple high (declarative) L* simple low (ynq) L*+H scooped, late rise (uncertainty/ incredulity) L+H* early rise to stress (contrastive focus) H+!H* fall onto stress (implied familiarity)

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•Downstepped accents:

•!H*, L+!H*, L*+!H

•Degree of prominence:within a phrase: HiF0

across phrases

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Functions of Pitch AccentFunctions of Pitch Accent

• Given/new information S: Do you need a return ticket.

U: No, thanks, I don’t need a return.

• Contrast (narrow focus) U: No, thanks, I don’t need a RETURN…. (I need

a time schedule, receipt,…)

• Disambiguation of discourse markers S: Now let me get you the train information.

U: Okay (thanks) vs. Okay….(but I really want…)

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Prosodic Phrasing in ToBIProsodic Phrasing in ToBI

• ‘Levels’ of phrasing: intermediate phrase: one or more pitch

accents plus a phrase accent (H- or L- ) intonational phrase: 1 or more intermediate

phrases + boundary tone (H% or L% )

• ToBI break-index tier 0 no word boundary 1 word boundary

2 strong juncture with no tonal markings

3 intermediate phrase boundary 4 intonational phrase boundary

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Functions of PhrasingFunctions of Phrasing

• Disambiguates syntactic constructions, e.g. PP attachment: S: You should buy the ticket with the discount

coupon.

• Disambiguates scope ambiguities, e.g. Negation: S: You aren’t booked through Rome because

of the fare.

• Or modifier scope: S: This fare is restricted to retired politicians

and civil servants.

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Contours: Accent + Contours: Accent + PhrasingPhrasing

• What do intonational contours ‘mean’ (Ladd ‘80, Bolinger ‘89)? Speech acts (statements, questions, requests)

S: That’ll be credit card? (L* H- H%)

Propositional attitude (uncertainty, incredulity)

S: You’d like an evening flight. (L*+H L- H%)

Speaker affect (anger, happiness, love)

U: I said four SEVEN one! (L+H* L- L%)

“Personality”

S: Welcome to the Sunshine Travel System.

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Pitch Range and TimingPitch Range and Timing

• Level of speaker engagement S: Welcome to InfoTravel. How may I help you?

• Contour interpretation S: You can take the L*+H bus from Malpensa to

Rome L-H%.

U: Take the bus. vs. Take the bus!

• Discourse/topic structure

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Can systems make use of this Can systems make use of this information?information?

Can they produce it??Can they produce it??

Can they recognize it??Can they recognize it??

intonational variation in spoken dialogue systems

Documents

dialogue agent

speech generationprosody

user turns

pace of interaction

interaction pace

function of task success

model user satisfaction

task solution