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

Coupling between ASR and MT in Speech-to-Speech Translation. Arthur Chan Prepared for Advanced Machine Translation Seminar. This Seminar. Introduction (6 slides) Ringger’s categorization of Coupling between ASR and NLU (7 slides) Interfaces in Loose Coupling 1 best and N-best (5 slides)

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

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  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) • Ringger’s 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 • Ney’s Theory and 2 methods of Implementation (14 slides) •  Sorry, without FST approaches. • Some “As Is” Ideas on This Topic

  3. 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.

  4. A Conceptual Model of Speech-to-Speech Translation Speech Recognizer Machine Translator Speech Synthesizer Decoding Result(s) Translation waveforms waveforms

  5. 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 (?)

  6. Scope of this talk. Speech Recognizer Machine Translator Speech Synthesizer 1-best? N-best? Translation waveforms waveforms Lattice? Confusion network?

  7. 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? • Ney’s Formulation

  8. 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 doesn’t know enough.

  9. The Concept of Coupling

  10. 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?

  11. Tightness of Coupling Tight Semi-Tight Loose

  12. 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

  13. 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)

  14. 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?

  15. Summary of Coupling between ASR and NLU

  16. 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) • (Matusov 2005) • Tight coupling • (Ney 1999) • (Casacuberta 2002) • Semi-tight coupling • (Quan 2005)

  17. Interfaces in Loose Coupling:1-best and N-best

  18. 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?)

  19. 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 (Next page)

  20. Note on 1-best • 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.

  21. 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

  22. Interfaces in Loose Coupling:Lattice, Consensus Network and Confidence Estimation

  23. What is 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.

  24. 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]

  25. 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.

  26. 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)

  27. 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.

  28. 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.

  29. Confusion Network and Consensus Hypothesis • Confusion Network: • Or “Sausage Network”. • Or “Consensus Network”

  30. 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 • ? • ?

  31. How to generate consensus network? • From the lattice • Summary of Mangu’s algorithm Intra-word clustering Inter-word clustering

  32. 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.

  33. Other ways to generate confusion network • From the N-best list • Using Rover. • A mixture of voting and adding confidence of word

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

  35. Interfaces in Loose Coupling:Results from the Literature

  36. 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.

  37. N-best list (Quan 2005) • Using N-best list for reranking • Interpolation weights of AM and TM are then optimized. • Summary: • Reranking gives improvements.

  38. 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.

  39. LWER against Lattice Density

  40. Modified Bleu scores against lattice density

  41. Optimal density and score weight based on Utterance Length.

  42. Consensus Network • Bertoldi 2005 is probably the only work on confusion-network based method • Summary of results: • When direct modeling is applied • Consensus Network doesn’t beat N-best method. • Author argues for speed and simplicity of the algorithm

  43. 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.

  44. 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.

  45. Tight Coupling : Theory and Practice

  46. Theory (Ney 1999) Baye’s Rule Introduce f as hidden var. Baye’s Rule Assume x doesn’t depend on target lang. Sum to Max

  47. Layman point of view • Three factors • Pr(e) : target language model • Pr(f|e) : translation model • Pr(x|f) : acoustic model • Note: assumption has been made only the best matching f for e is used.

  48. Comparison with SR • In SR: • Pr(f) : Source language model • In Tight coupling • Pr(f|e), Pr(e) : Translation model and Target language model

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