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Handing Uncertain Observations in Unsupervised Topic-MixtureLanguage Model Adaptation
Ekapol Chuangsuwanich1, Shinji Watanabe2,Takaaki Hori2, Tomoharu Iwata2, James Glass1
報告者:郝柏翰2013/03/05
ICASSP 2012
1MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA2NTT Communication Science Laboratories, NTT Corporation, Japan
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Outline
• Introduction
• Topic Tracking Language Model(TTLM)
• TTLM Using Confusion Network Inputs(TTLMCN)
• Experiments
• Conclusion
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Introduction
• In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes.
• To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention.
• This paper proposes a topic tracking language model that can adaptively track changes in topics based on current text information and previously estimated topic models in an on-line manner.
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TTLM
• Tracking temporal changes in language environments
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TTLM
• A long session of speech input is divided into chunks
• Each chunk is modeled by different topic distributions
• The current topic distribution depends on the topic distribution of the past H chunks and precision parameters α as follows:
Tt ,...,2,1
Kktkt 1
K
ktk
Hhthhtt
tkP1
1)ˆ*(1)},ˆ{|(
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TTLM
• With the topic distribution, the unigram probability of a word wm in the chunk can be recovered using the topic and word probabilities
• Where θ is the unigram probabilities of word wm in topic k
• The adapted n-gram can be used for a 2nd pass recognition for better results.
K
k kwtkm mwP1ˆ)(
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TTLMCN
• Consider a confusion network with M word slots.
• Each word slot m can contain different number of arcs Am
• with each arc containing a word wma and a corresponding arc posterior dma.
• Sm is binary selection parameter, where sm = 1 indicates that the arc is selected.
chunk1 chunk2 chunk3
slot1 slot2 slot3
A1=3 …
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TTLMCN
ma
mama
mam
mma
m
swe
swz
A
a
sma
N
mtzttettt dDSZWP 1),,,|,,(
• For each chunk t, we can write the joint distribution of words, latent topics and arc selections conditioned on the topic probabilities, unigram probabilities, and arc posteriors as follows:
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TTLMCN
• Graphical representation of TTLMCN
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Experiments(MIT-OCW)
• MIT-OCW is mainly composed of lectures given at MIT. Each lecture is typically two hours long. We segmented the lectures using Voice Activity Detectors into utterances averaging two seconds each.
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Compare with TTLM and TTLMCN
• We can see that the topic probability of TTLMCNI is more similar to the oracle experiment than TTLM, especially in the low probability regions.
• KL between TTLM and ORACLE was 3.3, TTLMCN was 1.3
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Conclusion
• We described an extension for the TTLM in order to handle errors in speech recognition. The proposed model used a confusion network as input instead of just one ASR hypothesis which improved performance even in high WER situations.
• The gain in word error rate was not very large since the LM typically contributed little to the performance of LVCSR.
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Significance Test (T-Test)
H0:實驗組與對照組的常態分佈一致H1:實驗組與對照組的常態分佈不一致
?
?t
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Significance Test (T-Test)
• Significance Test (T-Test)
• Example X 5 7 5 3 5 3 3 9
Y 8 1 4 6 6 4 1 2
5,40 xx M
4,32 yy M
571.42 xS
571.62 yS
)/( nSXt
8571.6
8571.4
45
B10,1,2):B1A10,:T.TEST(A1