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Language Identification. Oldřich Plchot, Pavel Ma t ějka Speech@FIT, Brno University of Technology, Czech Republic [email protected]. IKR Brno 2012. Outline. Why do we need LID? Evaluations Acoustic LID Phonotactic LID Fusion Conclusion. Wh y do we need language identification?. - PowerPoint PPT Presentation
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Language Identification
Oldřich Plchot, Pavel Matějka Speech@FIT, Brno University of Technology, Czech Republic
IKRBrno2012
Language Identification IKR, Brno, 2012
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Outline
• Why do we need LID?• Evaluations• Acoustic LID• Phonotactic LID• Fusion• Conclusion
Language Identification IKR, Brno, 2012
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Why do we need language identification?
1) Route phone calls to human operators.
Emergency (112,155,911) Call centers
Fireguard (150)Police (158)
Language Identification IKR, Brno, 2012
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Why do we need language identification?
2) Pre-select suitable recognition system.
Translate SPA
KWS CHN
Speech2Text ENG
Language Identification
Translate CZETranslate VIE
Connect
Language Identification IKR, Brno, 2012
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Why do we need language identification?
3) Security applications to narrow search space.
Language Identification IKR, Brno, 2012
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Two main approaches to LID
• Acoustic – Gaussian Mixture Model
• Phonotactic – Phoneme Recognition followed by Language Model
Language Identification IKR, Brno, 2012
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Acoustic approach
• Gaussian Mixture Model
- good for short speech segments and dialect recognition- relies on the sounds
Language Identification IKR, Brno, 2012
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-11.20.4
-4.7-13.0
2.34.5…
Spectral features - MFCC
20ms 10ms
Short-timeFFT
Mel - Filter Bank
Log () Discrete Cosine Transform
-12.8-0.3-5.7
-22.48.96.8…
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Shifted delta cepstra
• Shifted Delta Cepstra represent an information about the speech evolution around the current frame ( ± 0.1sec)
• Size of Final feature vector is: 7 MFCC + 7 × 7 SDC = 56
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Acoustic systems – GMM based
• Maximum likelihood (generative)• Objective function to maximize is the likelihood of
training data given the transcription
• Maximum Mutual Information (discriminative)• Objective function to maximize is the posterior
probability of all training utterances being correctly recognized
• Advantages of using discriminative training:• Lower error rates• Less parameters
• Disadvantages of discriminative training• Overtraining• Sometimes computationaly expensive
• Channel Compensation – from previous presentation
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Highly overlapped distributions
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Results on LRE 2007 (14 languages)
System / Equal Error Rate [%] 30sec 10sec 3sec
GMM2048 8.03 12.89 21.77
GMM2048-eigchan 2.76 7.38 17.14
GMM2048–chcf 2.94 7.40 17.93
GMM2048-MMI-chcf ( ~3 MMI iterations) 2.41 7.02 16.90
The best acoustic system combines:• Many Gaussians• Eigen-channel compensation of features• MMI
System / Equal Error Rate [%] 30sec 10sec 3sec
GMM2048 ML 8.03 12.89 21.77
GMM 256 ML ~16
GMM256 MMI (~15 MMI iterations) 4.15 8.61 18.43
GMM256-MMI-chcf (~3 MMI iterations) 3.73 9.81 20.98
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Phonotactic approach
• Phoneme Recognition followed by Language Model (PRLM)
- good for longer speech segments- robust against dialects in one language - eliminates speech characteristics of speaker's native language
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Phone recognizer
• 3 neural networks to produce the phone posterior probability
• 310 ms long time trajectory around the actual frame
• Investigation of different phone recognizers for LID => better phone recognizer ≈ better LID system
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Phone recognition output
One best phone string
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Phonotactic modeling - example
u n d 25
a n d 3
t h e 0
. . . .
u n d 1
a n d 32
t h e 13
. . . .
u n d 5
a n d 0
t h e 1
. . . .
German English Test
• N-gram language models – discounting, backoff • Support Vector Machines – vectors with counts• PCA + LDA• Neural Networks
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Phone recognition output
One best phone string
Phone lattice0,6
0,30,1
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Results on LRE 2007 (14 languages)
Conclusion:• Build as good phone recognizer as you can• Gather as much data for each language as you can• Different approaches to modeling counts seem to not have
big influence on results
System / Equal Error Rate [%] 30 sec 10 sec 3 sec
HU_LM string (4-gram) 6.35 13.86 27.12
HU_LM 5.54 11.75 23.54
HU_SVM-3gram-counts 5.41 13.26 26.92
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Fusion - LRE 2007 (14 languages)
System / Equal Error Rate [%] 30 sec 10 sec 3 sec
Acoustic - GMM2048-MMI-chcf ( ~3 MMI iterations)
2.41 7.02 16.90
Phonotactic - EN_TREE 3.54 10.68 22.66
Phonotactic - HU_TREE_A3E7M5S3G3_LFA 4.52 10.35 23.66
Fusion – The best 3 systems 1.28 4.63 13.53
Note:• Fusion weights have to be trained on separate set of files
which are as close as possible to target data
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Thanks for your attentionand
I hope you enjoyed it ;)
