Topic-independent Speaking-Style Transformation of Language model for Spontaneous Speech Recognition
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Topic-independent Speaking-Style Transformation of Language model for Spontaneous Speech Recognition. Yuya Akita , Tatsuya Kawahara. Introduction. Spoken-style v.s. writing style Combination of document and spontaneous corpus Irrelevant linguistic expression Model transformation

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Yuya Akita , Tatsuya Kawahara

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Yuya akita tatsuya kawahara

Topic-independent Speaking-Style Transformation of Language model for Spontaneous Speech Recognition

Yuya Akita , Tatsuya Kawahara


Introduction

Introduction

  • Spoken-style v.s. writing style

    • Combination of document and spontaneous corpus

      • Irrelevant linguistic expression

    • Model transformation

      • Simulated spoken-style text by randomly inserting fillers

      • Weighted finite-state transducer framework (?)

      • Statistical machine translation framework

  • Problem with Model transformation methods

    • Small corpus, data sparseness

    • One of solutions:

      • POS tag


Statistical transformation of language model

Statistical Transformation of Language model

  • Posteriori:

    • X: source language model (document style)

    • Y: target language model (spoken language)

  • So,

    • P(X|Y) and P(Y|X) are transformation model

  • Transformation models can be estimated using parallel corpus

    • n-gram count:


Statistical transformation of language model cont

Statistical Transformation of Language model (cont.)

  • Data sparseness problem for parallel corpus

    • POS information

      • Linear interpolation

      • Maximum entropy


Training

Training

  • Use aligned corpus

    • Word-based transformation probability

    • POS-based transformation probability

    • Pword(x|y) and PPOS(x|y) are estimated accordingly


Training cont

Training (cont.)

  • Back-off scheme

  • Linear interpolation scheme

  • Maximum entropy scheme

    • ME model is applied to every n-gram entry of document-style model

    • spoken-style n-garm is generated if transform probability is larger than a threshold


Experiments

Experiments

  • Training coprus:

    • Baseline corpus: National Congress of Japan, 71M words

    • Parallel corpus: budget committee in 2003, 666K

    • Corpus of Spontaneous Japan, 2.9M words

  • Test corpus:

    • Another meeting of Budget committee in 2003, 63k words


Experiments cont

Experiments (cont.)

  • Evaluation of Generality of transformation model

  • LM


Experiments cont1

Experiments (cont.)

  • r


Conclusions

Conclusions

  • Propose a novel statistical transformation model approach


Non stationary n gram model

Non-stationary n-gram model


Concept

Concept

  • Probability of sentence

    • n-gram LM

  • Actually,

  • Miss long-distance and word position information while applying Markov assumption


Concept cont

Concept (cont.)


Training cont1

Training (cont.)

  • ML estimation

  • Smoothing

    • Use low order

    • Use small bins

    • Transform with

      Smoothed normal ngram

  • Combination

    • Linear interpolation

    • Back-off


Smoothing with lower order cont

Smoothing with lower order (cont.)

  • Additive smoothing

  • Back-off smoothing

  • Linear interpolation


Smoothing with small bins k 1 cont

Smoothing with small bins (k=1) (cont.)

  • Back-off smoothing

  • Linear interpolation

  • Hybrid smoothing


Transformation with smoothed ngram

Transformation with smoothed ngram

  • Novel method

    • If t-mean(w) decreases, the word is more important

    • Var(w) is used to balance t-mean(w) for active words

    • active word: words can appears at any position in the sentences

  • Back-off smoothing & linear interpolation


Experiments1

Experiments

Observation: Marginal position & middle position


Experiments cont2

Experiments (cont.)

  • NS bigram


Experiments cont3

Experiments (cont.)

  • Comparison with three smoothing techniques


Experiments cont4

Experiments (cont.)

  • Error rate with different bins


Conclusions1

Conclusions

  • Traditional n-gram model is enhanced by relaxing its stationary hypothesis and exploring the word positional information in language modeling


Two way poisson mixture model

Two-way Poisson Mixture model


Essential

Document x

Class k

X1

X2

Poisson 1

...

πk1

xp

πk2

Poisson 2

Σ

πkRk

Poisson Rk

Essential

  • Poisson distribution

  • Poisson mixture model

Multivariate Poisson, dim = p (lexicon size)

*Word clustering:

reduce Poisson dimension

=> Two-way mixtures


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