Spoken Dialogue Systems
CS 4705
Talking to a Machine….and
(often) Getting an Answer
• Today’s spoken dialogue systems make it possible
to accomplish real tasks without talking to a
person
– Could Eliza do this?
– What do today’s systems do better?
– Do they actually embody human intelligence?
• Key advances
–
–
–
–
Stick to goal-directed interactions in a limited domain
Prime users to adopt the vocabulary you can recognize
Partition the interaction into manageable stages
Judicious use of system vs. mixed initiative
Dialogue vs. Monologue
• Monologue and dialogue both involve interpreting
–
–
–
–
Information status
Coherence issues
Reference resolution
Speech acts, implicature, intentionality
• Dialogue involves managing
– Turn-taking
– Grounding and repairing misunderstandings
– Initiative and confirmation strategies
Segmenting Speech into Utterances
• What is an `utterance’?
– Why is EOU detection harder than EOS?
– How does speech differ from text?
– Single syntactic sentence may span several turns
A: We've got you on USAir flight 99
B: Yep
A: leaving on December 1.
– Multiple syntactic sentences may occur in single turn
A: We've got you on USAir flight 99 leaving on December. Do
you need a rental car?
– Intonational definitions: intonational phrase, breath
group, intonation unit
Turns and Utterances
• Dialogue is characterized by turn-taking: who
should talk next, and when they should talk
• How do we identify turns in recorded speech?
– Little speaker overlap (around 5% in English --although
depends on domain)
– But little silence between turns either
• How do we know when a speaker is giving up or
taking a turn? Holding the floor? How do we
know when a speaker is interruptable?
Simplified Turn-Taking Rule (Sacks et al)
• At each transition-relevance place (TRP) of each
turn:
– If current speaker has selected A as next speaker, then A
must speak next
– If current speaker does not select next speaker, any
other speaker may take next turn
– If no one else takes next turn, the current speaker may
take next turn
• TRPs are where the structure of the language
allows speaker shifts to occur
• Adjacency pairs set up next speaker expectations
–
–
–
–
GREETING/GREETING
QUESTION/ANSWER
COMPLIMENT/DOWNPLAYER
REQUEST/GRANT
• ‘Significant silence’ is dispreferred
A: Is there something bothering you or not? (1.0s)
A: Yes or no? (1.5s)
A: Eh?
B: No.
Intonational Cues to Turntaking
• Continuation rise (L-H%) holds the floor
• H-H% requests a response
– L*H-H% (ynq contour)
– H* H-H% (highrise question contour)
• Intonational contours signal dialogue acts in
adjacency pairs
Timing and Turntaking
• How should we time responses in a SDS?
– Japanese studies of aizuchi (backchannels) (Koiso et al
‘98, Takeuchi et al ‘02) in natural speech
– Lexical information: particles ne and ka ending
preceding turn or (in telephone shopping) product
names
– Length of preceding utterance, f0, loudness, and pause
after even more important in predicting turntaking
Turntaking and Initiative Strategies
• System Initiative
S: Please give me your arrival city name.
U: Baltimore.
S: Please give me your departure city name….
• User Initiative
S: How may I help you?
U: I want to go from Boston to Baltimore on November 8.
• `Mixed’ initiative
S: How may I help you?
U: I want to go to Boston.
S: What day do you want to go to Boston?
Grounding (Clark & Shaefer ‘89)
• Conversational participants don’t just take turns
speaking….they try to establish common ground
(or mutual belief)
• Hmust ground a S's utterances by making it clear
whether or not understanding has occurred
• How do hearers do this?
S: I can upgrade you to an SUV at that rate.
– Continued attention
(U gazes appreciatively at S)
– Relevant next contribution
U: Do you have a RAV4 available?
– Acknowledgement/backchannel
U: Ok/Mhmmm/Great!
– Demonstration/paraphrase
U: An SUV.
– Display/repetition
U: You can upgrade me to an SUV at the same rate?
– Request for repair
U: I beg your pardon?
Detecting Grounding Behavior
• 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
• User information state reflected in response
(Shimojima et al ’99, ‘01)
– Echoic responses repeat prior information – as
acknowledgment or request for confirmation
S1: 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
Grounding and Confirmation Strategies
U: I want to go to Baltimore.
• Explicit
S: Did you say you want to go to Baltimore?
• Implicit
S: Baltimore. (H* L- L%)
S: Baltimore? (L* H- H%)
S: What time do you want to leave Baltimore?
• No confirmation
How do we evaluate 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 accomplished
Maximize
Task Success
Minimize
Costs
Efficiency
Measures
Qualitative
Measures
What metrics should we use?
• 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
7/15/2016
17
User Satisfaction:
Sum 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?
7/15/2016
(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)
18
Performance Model
• Weights trained for each independent factor via
multiple regression modeling: how much does
each contribute to User Satisfaction?
• Result 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’
Identifying Misrecognitions, Awares and User
Corrections Automatically (Hirschberg,
Litman & Swerts)
• Collect corpus from interactive voice response
system
• Identify speaker ‘turns’
• incorrectly recognized
• where speakers first aware of error
• that correct misrecognitions
• Identify prosodic features of turns in each
category and compare to other turns
• Use Machine Learning techniques to train a
classifier to make these distinctions automatically
Turn Types
TOOT: Hi. This is AT&T Amtrak Schedule
System. This is TOOT. How may I help you?
User: Hello. I would like trains from
Philadelphia to New York leaving on Sunday at
ten thirty in the evening.
misrecognition
TOOT: Which city do you want to go to?
User: New York.
correction
aware site
Results
• Reduced error in predicting misrecognized turns to
8.64%
• Error in predicting ‘awares’ (12%)
• Error in predicting corrections (18-21%)
Conclusions
• Spoken dialogue systems presents new problems - but also new possibilities
– Recognizing speech introduces a new source of errors
– Additional information provided in the speech stream
offers new information about users’ intended meanings,
emotional state (grounding of information, speech acts,
reaction to system errors)
• Why spoken dialogue systems rather than webbased interfaces?