Coupling between ASR and MT in Speech-to-Speech Translation

1
Coupling between ASR and MT in Speech-to-Speech
Translation

• Arthur Chan
• Prepared for
• Advanced Machine Translation Seminar

2
This Seminar

• Introduction (6 slides)
• Ringgers categorization of Coupling between ASR
  and NLU (7 slides)
• Interfaces in Loose Coupling
• 1 best and N-best (5 slides)
• Lattices/Confusion Network/Confidence Estimation
  (12 slides)
• Results from literature
• Tight Coupling
• Neys Theory and 2 methods of Implementation (14
  slides)
• ? Sorry, without FST approaches.
• Some As Is Ideas on This Topic

3
History of this presentation

• V1
• Draft finished in Mar 1st
• Tanjas comment
• Direct modeling could be skipped.
• We could focus on more ASR-related issue.
• Issues in MT search could be ignored.

4
History of this presentation (cont.)

• V2 V4
• Follow Tanjas comment and finished in Mar 19th .
• Reviewers comment
• Neys search formulation is too difficult to
  follow
• FST-based tight coupling method is important. We
  should cover it.
• V5
• Reviewed another 5 papers solely on the issue of
  FST-based tight-coupling. (True coupling)

5
6 papers on Coupling of Speech-to-Speech
Translation

• H. Ney, Speech translation Coupling of
  recognition and translation, in Proc. ICASSP,
  1999.
• Casacuberta et al., Architectures for
  speech-to-speech translation using finite-state
  models, in Proc. Workshop on Speech-to-Speech
  Translation, 2002.
• E. Matusov, S.Kanthak, and H. Ney, On the
  integration of speech recognition and statistical
  machine translation, in Proc. InterSpeech,
  2005.
• S.Saleem, S. C. Jou, S. Vogel, and T. Schultz,
  Using word lattice information for a tighter
  coupling in speech translation systems, in Proc.
  ICSLP, 2004.
• V.H. Quan et al., Integrated N-best re-ranking
  for spoken language translation, in In
  EuroSpeech, 2005.
• N. Bertoldi and M. Federico, A new decoder for
  spoken language translation based on confusion
  networks, in IEEE ASRU Workshop, 2005.

6
A Conceptual Model of Speech-to-Speech Translation
Speech Recognizer
Machine Translator
Speech Synthesizer
Decoding Result(s)
Translation
waveforms
waveforms
7
Motivation of Tight Coupling between ASR and MT

• One best of ASR could be wrong
• MT could be benefited from wide range of
  supplementary information provided by ASR
• N-best list
• Lattice
• Sentenced/Word-based Confidence Scores
• E.g. Word posterior probability
• Confusion network
• Or consensus decoding (Mangu 1999)
• MT quality may depend on WER of ASR (?)

8
Scope of this talk.
Speech Recognizer
Machine Translator
Speech Synthesizer
1-best?
Translation
N-best?
waveforms
waveforms
Lattice?
Confusion network?
9
Topics Covered Today

• The concept of Coupling
• Tightness of coupling between ASR and
  Technology X. (Ringger 95)
• Two questions
• What could ASR provide in loose coupling?
• Discussion of interfaces between ASR and MT in
  loose coupling
• What is the status of tight coupling?
• Neys Formulation

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Topics not covered

• Direct Modeling
• Use both features in ASR and MT
• Some referred as ASR and MT unification
• Implication of the MT search algorithms on the
  coupling
• Generation of speech from text.
• Presenter doesnt know enough.

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The Concept of Coupling
12
Classification of Coupling of ASR and Natural
Language Understanding (NLU)

• Proposed in Ringger 95, Harper 94
• 3 Dimensions of ASR/NLU
• Complexity of the search algorithm
• Simple N-gram?
• Incrementality of the coupling
• On-line? Left-to-right?
• Tightness of the coupling
• Tight? Loose? Semi-tight?

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Tightness of Coupling
Tight
Semi-Tight
Loose
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Notes

• Semi-tight coupling could appear as
• Feedback loop between ASR and Technology X for
  the whole utterance of speech
• Or Feedback loop between ASR and Technology X for
  every frame.
• The Ringger system
• A good way to understand how speech-based system
  is developed

15
Example 1 LM

• Someone asserts that ASR has to be used with
  13-grams.
• In tight-coupling,
• A search will be devised to search for the best
  word sequence with best acoustic score 13 gram
  likelihood
• In loose coupling
• A simple search will be used to generate some
  outputs (N-best list, lattice etc.),
• 13-gram will then use to rescore the output.
• In semi-tight coupling
• 1, A simple search will be used to generate
  results
• 2, 13 gram will be applied at the word-end only
  (but exact history will not be stored)

16
Example 2 Higher order AM

• Segmental model assume obs. probability is not
  conditionally independent.
• Someone assert that segmental model is better
  than just HMM.
• Tight coupling Direct search of the best word
  sequence using segmental model.
• Loose coupling Use segmental model to rescore
• Semi-tight coupling Hybrid HMM-Segmental model
  algorithm?

17
Summary of Coupling between ASR and NLU
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Implication on ASR/MT coupling

• Generalize many systems
• Loose coupling
• Any system which uses 1-best, n-best, lattice, or
  other inputs for 1-way module communication
• (Bertoldi 2005)
• CMU System (Saleem 2004)
• Tight coupling
• (Ney 1999)
• (Matusov 2005)
• (Casacuberta 2002)
• Semi-tight coupling
• (Quan 2005)

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Interfaces in Loose Coupling1-best and N-best
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Perspectives

• ASR outputs
• 1-best results
• N-best results
• Lattice
• Consensus network.
• Confidence scores
• How ASR generate these outputs?
• Why they are generated?
• What if there are multiple ASRs?
• (and what if their results are combined?)
• Note we are talking about state-lattice now,
  not word-lattice. ?

21
Origin of the 1-best.

• Decoding of HMM-based ASR
• Searching the best path in a huge HMM-state
  lattice.
• 1-best ASR result
• The best path one could find from backtracking.
• State Lattice in ASR (Next page)

22
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Note on 1-best in ASR

• Most of the time 1-best Word Sequence
• Why?
• In LVCSR, storing the backtracking pointer table
  for state sequence takes a lot of memory (even
  nowadays)
• Compare this with the number of frames of score
  one need to be stored
• Usually a backtrack pointer storing
• The previous words before the current word
• Clever structure dynamically allocate
  back-tracking pointer table.

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What is N-best list?

• Traceback not only from the 1st -best, also from
  the 2nd best and 3rd best, etc.
• Pathway
• Directly from search backtrack pointer table
• Exact N-best algorithm (Chow 90)
• Word pair N-best algorithm (Chow 91)
• A search using Viterbi score as heuristic (Chow
  92)
• Generate lattice first, then generate N-best from
  lattice

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Interfaces in Loose CouplingLattice, Consensus
Network and Confidence Estimation
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What is Lattice?

• A word-based lattice
• A compact representation of state-lattice
• Only word node (or link) are involved
• Difference between N-best and Lattice
• Lattice could be compact representation of N-best
  list.

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How lattice is generated?

• From the decoding backtracking pointer table
• Only record all the links between word nodes.
• From N-best list
• Become a compact representation of N-best
• Sometimes spurious link will be introduced

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How lattice is generated when there are phone
contexts at the word end?

• Very complicated when phonetic context is
  involved
• Not only word-end needs to be stored but also the
  phone contexts.
• Lattice has the word identity as well as contexts
• Lattice can become very large.

30
How this is resolved?

• Some used only approximate triphone to generate
  lattice in first stage (BBN)
• Some generate lattice even with full CD-phones
  but convert it back to no-context lattices (RWTH)
• Use the lattice with full CD phone contexts
  (RWTH)

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What ASR folks do when lattice is still too large?

• Use some criteria to prune the lattice.
• Example Criteria
• Word posterior probability
• Application of another LM or AM, then filtering.
• General confidence score
• Maximum lattice density
• (number of words in lattice/number of words)
• Or generate an even more compact representation
  than lattices
• E.g. consensus network.

32
Conclusions on lattices

• Lattice generation itself could be a complicated
  issue
• Sometimes, what post-processing stage (e.g. MT)
  will get is pre-filtered, pre-processed results.

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Confusion Network and Consensus Hypothesis

• Confusion Network
• Or Sausage Network.
• Or Consensus Network

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Special Properties (?)

• More local than lattice
• One can apply simple criteria to find the best
  results
• E.g. consensus decoding is to apply
  word-posterior probability on confusion network.
• More tractable
• In terms of size
• Found to be useful in
• ?
• ?

35
How to generate consensus network?

• From the lattice
• Summary of Mangus algorithm
• Intra-word clustering
• Inter-word clustering

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Note on Consensus Network

• Note
• Time information might not be preserved in
  confusion network
• The similarity function directly affect the final
  output of the consensus network.

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Other ways to generate confusion network

• From the N-best list
• Using Rover.
• A mixture of voting and adding confidence of word

38
Confidence Measure

• Anything other than likelihood which could tell
  whether the answer is useful
• E.g.
• Word posterior probability
• P(WA)
• Usually compute using lattices
• Language model backoff mode
• Other posterior probabilities (frame, sentence)

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Interfaces in Loose CouplingResults from the
Literature
40
General word

• Coupling in SST is still pretty new
• Papers are chosen according to whether some
  outputs have been used
• Other techniques such as direct modeling might be
  mixed into the papers.

41
N-best list (Quan 2005)

• Using N-best list for reranking
• Interpolation weights of AM and TM are then
  optimized.
• Summary
• Reranking gives improvements.

42
Lattices CMU results (Saleem 2004)

• Summary of results
• Lattice word error rate improved when lattice
  density improves
• Lattice density and Weight on Acoustic scores
  turns out to be an important parameter to tune
• Too large and small could hurt.

43
LWER against Lattice Density
44
Modified Bleu scores against lattice density
45
Optimal density and score weight based on
Utterance Length.
46
Consensus Network

• Bertoldi 2005 is probably the only work on
  confusion-network based method
• Summary of results
• When direct modeling is applied
• Consensus Network doesnt beat N-best method.
• Author argues for speed and simplicity of the
  algorithm

47
Confidence Does it help?

• According to Zhang 2006, Yes.
• Confidence Measure (CM) filtering is used to
  filter out unnecessary results in N-best
• Note The approaches used is quite different.

48
Conclusion on Loose Coupling

• SR could give a rich sets of output.
• It is still an unknown what type of output should
  be used in pipeline.
• Currently, it seem to lack of comprehensive
  experimental studies on which method is the best.
  
• Usage of confusion network and confidence
  estimation seem to be under-explored.

49
Tight Coupling Theory and Practice
50
Theory (Ney 1999)
Bayes Rule
Introduce f as hidden var.
Bayes Rule
Assume x doesnt depend on target lang.
Sum to Max
51
Layman point of view

• Three factors
• Pr(e) target language model
• Pr(fe) translation model
• Pr(xf) acoustic model
• Note assumption has been made only the best
  matching f for e is used.

52
Comparison with SR

• In SR
• Pr(f) Source language model
• In Tight coupling
• Pr(fe), Pr(e) Translation model and Target
  language model

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Algorithmic Point of View

• Brute Force Method Instead of incorporating LM
  into standard Viterbi algorithm
• Incoporating P(e) and P(fe)
• gt Very complicated
• The backup slides in the presentation has detail
  about Neys implementations.

54
Experimental Results in Matusov, Kanthak and Ney
2005

• Summary of the results
• Translation quality is only improved by tight
  coupling when the lattice density is not high.
• Same as Saleem 2004, incorporation of acoustic
  scores help.

55
Conclusion Possible Issues of tight coupling

• Possibilities
• In SR, source n-gram LM is very closed to the
  best configuration.
• The complexity of the algorithm is too high,
  approximation is still necessary to make it work.
• When the criterion in tight coupling is used. It
  is possible that the LM and the TM need to be
  jointly estimated.
• The current approaches still havent really
  implement tight-coupling
• There might be bugs in the programs.

56
Conclusion

• Two major issues in coupling of SST is discussed
• In loose coupling
• Consensus network and Confidence scoring is still
  not fully utilized
• In tight coupling
• The approach seem to be haunted by very high
  complexity of search algorithm construction

57
Discussion
58
The End. Thanks.
59
Literature

• 2006 Ruiqiang Zhang, Genichiro Kikui. Integration
  of Speech Recognition and Machine Translation
  Speech Recognition Word Lattice Translation.
  Speech Communication. Vol.48, Issues 3-4
• H. Ney, Speech translation Coupling of
  recognition and translation, in Proc. ICASSP,
  1999.
• E. Matusov, S.Kanthak, and H. Ney, On the
  integration of speech recognition and statistical
  machine translation, in Proc. InterSpeech, 2005.
• S.Saleem, S. C. Jou, S. Vogel, and T. Schultz,
  Using word lattice information for a tighter
  coupling in speech translation systems, in Proc.
  ICSLP, 2004.
• V.H. Quan et al., Integrated N-best re-ranking
  for spoken language translation, in In
  EuroSpeech, 2005.
• N. Bertoldi and M. Federico, A new decoder for
  spoken language translation based on confusion
  networks, in IEEE ASRU Workshop, 2005.
• L. Mangu, E. Brill, A. Stolcke, Finding
  consensus in speech recognition word error
  minimization and other applications of confusion
  networks, Computer Speech and Language 14(4),
  373-400., (2000)
• E. Ringger, A Robust Loose Coupling for Speech
  Recognition and Natural Language Understanding,
  1995

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Backup Slides
61
Ney 99s Formulation of SSTs Search.
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Assumptions in Modeling

• Alignment Models (HMM)
• Acoustic Modeling
• Speech Recognizer will produce a word graph.
• Each link with word hypothesis covers the portion
  of acoustic scores. (notation is confusing in
  paper)

63
Lexicon Modeling

• Further assumption from standard IBM models
• Target word is assumed to be dependent on
  previous word
• So, in fact, source LM is actually there.

64
First Implementation Local Average Assumptions

• Local Average Assumptions
• P(xe) is used to capture the local
  characteristic of the acoustic.

65
Justification of Using Average Local Assumption

• Rephrased from Author (p.3 para 2)
• Lexicon modeling and language modeling will cause
  f_j-1, f_j, f_j1 appear in the math.
• In another words
• It is too complicated to carry out
• Computation advantage the local score could be
  obtained just from the word graph but before
  translation
• gt Full translation strategy could still be
  carried out

66
Computation of P(xe)

• Make use of best source sequence
• Also refer to Wessel 98,
• A commonly used word posterior probability
  algorithm for lattice
• A forward-backward like procedure is used

67
Second Method Monotone Alignment Assumption -
Network
68
Monotone Alignment Assumption Formula for Text
Input

• Close-formed solution exist form DP O(JE2)

69
Monotone Alignment Assumption Formula for
Speech Input

• DP
• O(JE2F2)

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How to make Monotone Assumptions work?

• Words needs to be reordered
• As part of search strategy.
• Does acoustic model assumption used?
• i.e. Are we talking about word lattice or still
  state lattice?
• Dont know, seems like we are actually talking
  about word lattice.
• Supported by Matusov 2005