dissertation defense saarbrücken – november ??th 2004 automatic classification of speech...
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Dissertation DefenseSaarbrücken – November ??th 2004
Automatic Classification of Speech Recognition Hypotheses Using
Acoustic and Pragmatic Features
Malte Gabsdil
Universität des Saarlandes
The Problem (theoretical)
• Grounding: establishing common ground between dialogue participants– “Did H correctly understand what S said?”
• Combination of bottom-up (“signal”) and top-down (“expectation”) information
• Clark (1996): Action ladders– upward completion– downward evidence
The Problem (practical)
• Assessment of recognition quality for spoken dialogue systems
• Information sources– speech/recognition output (“acoustic”)– dialogue/task context (“pragmatic”)
• Crucial for usability and user satisfaction– avoid misunderstandings– promote dialogue flow and efficiency
The General Picture
• Dialogue System Architecture
Dialogue Manager
dialoguehistory
interpretation
dialoguemodel
response selection
Gen
erat
ion
AS
R
A Closer Look• How to assess recognition quality?
– decision problem
AS
R
Dialogue Manager
dialoguehistory
interpretation
dialoguemodel
response selection
Besthypothesis+ confidence
Con
fiden
ce r
ejec
tion
thre
shol
ds
n-Besthypotheses+ confidence
Mac
hin
e L
earn
ing
Cla
ssif
ier
Pragmatic features
Acoustic features
Overview
• Machine learning classifiers
• Acoustic and pragmatic features
• Experiment 1: Chess– exemplary domain
• Experiment 2: WITAS– complex spoken dialogue system
• Conclusions & Topics for Future Work
Machine Learning Classifiers
• Concept learners– learn decision function – training: present feature vectors annotated
with correct class– testing: classify unseen feature vectors
• Combine acoustic and pragmatic features to classify recognition hypotheses as accept, (clarify), reject, or ignore
nccxf ,...,)( 1
Acoustic Information
• Derived from speech waveforms and recognition output
• Low level features– amplitude, pitch (f0), duration, tempo (e.g.
Levow 1998, Litman et al. 2000)
• Recogniser confidences– normalised probability that a sequence of
recognised words is correct (e.g. Wessel et al. 2001)
Pragmatic Information
• Derived from the dialogue context and task knowledge
• Dialogue features– adjacency pairs: current/previous dialogue
move, DM bigram frequencies– reference: unresolvable definite NPs/PROs
• Task features (scenario dependent)– evaluation of move scores (Chess), conflicts
in action preconds and effects of (WITAS)
Experiment 1: Chess
• Recognise spoken chess move instructions– speech interface to computer chess program
• Exemplary domain to test methodology– nice properties, easy to control
• Pragmatic features: automatic move evaluation scores (Crafty)
• Acoustic features: recogniser confidence scores (Nuance 8.0)
Data & Design
• Subjects replay given chess games– instruct each other to move pieces– approx. 2000 move instructions in different
data sets (devel, train, test)
• 5 x 2 x 6 design– 5 systems for classifying recognition results
(main effect)– 2 game levels (strong vs. weak)– 6 pairs of players
Players and Instructions
Systems
• Task: accept or reject rec. hypotheses
• Baseline– confidence rejection threshold– binary classification of best hypothesis
• ML System– SVM learner (best on dev. set)– binary classication of 10-best results– choose first classified as accept, else reject
Results
• Accuracy:– Baseline: 64.3%, ML System: 97.2%
0
200
400
600
800
Baseline ML System
false rejects
false accepts
correct rejects
correct accepts
Evaluation
• 82.2% relative error rate reduction
• χ² test on confusion matrices– highly significant (p<.001)
• Combination of acoustic and pragmatic information outperforms standard approach
• System reacts appropriately more often → increased usability
Experiment 2: WITAS
• Operator interaction with robot helicopter
• Multi-modal references, collaborative activities, multi-tasking
• Differences to chess experiment– complex dialogue scenario– complex system (ISU-based, planning, …)– much larger grammar and vocabulary
• Chess 37 GR, Vocab 50 FEHLT WAS
– open mic recordings (ignore class)
WITAS Screenshot
Data Preparation
• 30 dialogues (6 users, 303 utterances)
• Manual transcriptions
• Offline recognition (10best) and parsing→ quasi-logical forms
• Hypothesis labelling:– accept: same QLF– reject: out-of-grammar or different QLF– ignore: “crosstalk” not directed to system
Example Features
• Acoustic– low level: amplitude (RMS)– recogniser: hypothesis confidence score, rank
in nbest list
• Pragmatic– dialogue: current/previous DM, DM bigram
probability, #unresolvable definite NPs – task: #conflicts in planning operators (e.g.
already satisfied effects)
ResultsBaseline (Lemon 2004)
0
50
100
150
200
accept reject ignore
Best ML System
0
50
100
150
200
accept reject ignore
• Context-sensitive LMs• Accuracy: 65.68%• Wfscore: 61.81%• (higher price)
• TiMBL (optimised)• Accuracy: 86.14%• Wfscore: 86.39%
Evaluation
• 59.6% relative error rate reduction
• χ² test on confusion matrices– highly significant (p<.001)
• Combination of acoustic and pragmatic information outperforms grammar switching approach
• System reacts appropriately more often → increased usability
WITAS Features
• Importance according to χ²1. confidence (ac: recogniser)2. DMBigramFrequency (pr: dialogue)3. currentDM (pr: dialogue)4. minAmp (ac: low level)5. hypothesisLength (ac: recogniser)6. RMSamp (ac: low level)7. currentCommand (pr: dialogue)8. minWordConf (ac: recogniser)9. aqMatch (pr: dialogue)10. nbestRank (ac: recogniser)
Summary/Achievements
• Assessment of recognition quality for spoken dialogue systems (grounding)
• Combination of acoustic and pragmatic information via machine learning
• Highly significant improvements in classification accuracy over standard methods (incl. “grammar switching”)
• Expect better system behaviour and user satisfaction
Topics for Future Work
• Usability evaluation– systems with and w/o classification module
• Generic and system-specific features– which features are available across systems?
• Tools for ISU-based systems– module in DIPPER software library
• Clarification– flexible generation (alternative questions,
word-level clarification)
APPENDIX
Our Proposal
• Combine acoustic and pragmatic information in a principled way
• Machine learning to predict the grounding status of competing recognition hypotheses of user utterances
• Evaluation against standard methods in spoken dialogue system engineering– confidence rejection thresholds
Application
ASR
1.
2.
3.
4.
5.
6.
7.
8.
9.
…
MLclassifier
Acousticinformation
Pragmaticinformation
1.
2.
3.
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5.
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8.
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…
Dialogue Manager
dialogue contexttask knowledge
Data CollectionGame Pair1 Pair2 Pair3 Pair4 Pair5 Pair6 Total
Trial (1) (1) (1) (1) (1) (1)
LMw (2) (2) (3)
LMs (4) (5) (5)
DSw 69 (5) 68 (4) 62 (4) 199
DSs 65 (3) 68 (3) 62 (4) 200
TRw 68 (4) 64 (2) 66 (4) 60 (3) 69 (5) 62 (3) 389
TRs 70 (5) 63 (3) 70 (5) 59 (2) 62 (4) 68 (2) 392
TEw 64 (6) 64 (6) 64 (6) 64 (6) 64 (6) 64 (6) 384
TEs 69 (7) 69 (7) 69 (7) 69 (7) 69 (7) 69 (7) 414
Total 336 329 337 320 331 325 1978
Chess Results
• Accuracy:– Base: 64.3%, LM: 93.5%, ML: 97.2%
0
200
400
600
800
Baseline LegalMoves
MLSystem
false rejects
false accepts
correct rejects
correct accepts
Data/Baseline
• Data from user study with WITAS– 6 subjects, 5 tasks each (“open mic”)– 30 dialogues (303 user utterances)– recorded utterances and logs of WITAS
Information State (dialogue history)
• Originally collected to evaluate a “grammar switching” version of WITAS (= Baseline; Lemon 2004)
Data Preparation/Setup
• Manual transcription of all utterances
• Offline recognition (10best) with “full” grammar and processing with NLU component (quasi-logical forms)
• Hypothesis labelling:– accept: same QLF– reject: out-of-grammar or different QLF– ignore: “crosstalk” not directed to system
Acoustic Features
• Low level:– RMSamp, minAmp (abs), meanAmp (abs)– motiv: detect crosstalk
• Recogniser output/confidence scores:– nbest rank, hypothesisLength (words)– hyp. confidence, confidence zScore,
confidence SD, minWordConf– motiv: quality estimation within and across
hypotheses
Pragmatic Features
• Dialogue:– currentDM, DMTactiveNode, qaMatch,
aqMatch, DMbigramFreq, currentCommand– #unresNPs, #unresPROs, #uniqueIndefs– motiv: adjacency pairs, unlikely references
• Task:– taskConflict (same command already active),
taskConstraintConflict (fly vs. land)
Importance of Features• What are the most predictive features?
– χ² statistics: correlate feature values with different classes
– computed for each feature from value/class contingency tables
i j ij
ijij
E
OE )²(²
..
..
n
nnE
ijij
– Oij: observed frequencies; Eij: expected frequencies
– n.j: sum over column j; ni.: sum over row i;n..: #instances
Simple Example
c1 c2
v1 100 100 200
v2 100 100 200
v3 100 100 200
v4 100 100 200
400 400 n=800
• Feature B• Feature A
• χ² = 0
c1 c2
v1 20 80 100
v2 75 75 150
v3 20 130 150
v4 285 115 400
400 400 n=800
• χ² = 2*(30²/50)+2*0+ 2*(55²/75)+ 2*(85²/200) = 188.92