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Table 2.1: Constant 30% perplexity reduction translates to variable entropy reduction. PPL PPL after Relative PPL Entropy Entropy after Relative entropy reduction reduction [bits] reduction reduction 2 1.4 30% 1 0.49 51% 20 14 30% 4.32 3.81 11.8% 100 70 30% 6.64 6.13 7.7% 200 140 30% 7.64 7.13 6.7% 500 350 30% 8.97 8.45 5.8% 2000 1400 30% 10.97 10.45 4.7% 2.1.2 Word Error Rate The word error rate of speech recognizer is defined as W ER = S + D + I , (2.3) N where S is number of substitutions, D deletions and I insertions (each operation can change, delete or add a single word). The WER is defined for the lowest number of these operations that are needed to change the decoded utterance W ′ to the reference utterance W , which has N words. The word error rate (WER) measures directly the quality of the speech recognition system, by counting the number of mistakes between the output of the system and the reference transcription which is provided by a human annotator. The drawbacks include over-emphasis on uninformative words (which is usually reduced in advanced metrics that tolerate substitutions between words with the same sense, like NIST WER). For comparison of different techniques, word error rate can be inaccurate, and improvements are commonly misinterpreted by researchers. Practical experience shows that it is very hard to obtain improvements over well-tuned systems <strong>based</strong> on state-of-the-art techniques. Some techniques can yield large WER improvements when applied to simple systems, while they have practically no influence in the best systems. Comparison of relative WER reductions when applying different techniques to different systems is practically useless. On the other hand, comparing different techniques on the same task, or even better by using the same configuration of ASR system, can be very informative and WER can be a better metric than perplexity in such cases. To conclude usefulness of different metrics - the advantages of perplexity are: 14
• Good theoretical motivation • Simplicity of evaluation • Good correlation with system performance Disadvantages of perplexity are: • It is hard to check that the reported value is correct (mostly normalization and ”looking into future” related problems) • Perplexity is often measured assuming perfect history, while this is certainly not true for ASR systems: poor performance of models that rely on long context information (such as cache models) is source of confusion and claims that perplexity is not well correlated with WER • Most of the research papers compare perplexity values incorrectly - the baseline is often suboptimal to ”make the results look better” Advantages of WER: • Often the final metric we want to optimize; quality of systems is usually measured by some variation of WER (such as NIST WER) • Easy to evaluate, as long as we have reference transcriptions Disadvantages of WER: • Results are often noisy; for small data sets, the variance in WER results can be absolutely 0.5% • Overemphasis on the frequent, uninformative words • Reference transcriptions can include errors, spelling mistakes • Substituted words with the same or similar meaning are as bad mistakes as words that have the opposite meaning • Full speech recognition system is needed • Improvements are often task-specific 15
Page 1 and 2: VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ
Page 3 and 4: Abstrakt Statistické jazykové mod
Page 5 and 6: Contents 1 Introduction 4 1.1 Motiv
Page 7 and 8: 6.2.3 Reduction of Vocabulary Size
Page 9 and 10: Maybe the most popular vision of fu
Page 11 and 12: Chapter 6 presents further extensio
Page 13 and 14: Chapter 2 Overview of Stati
Page 15 and 16: 2.1 Evaluation 2.1.1 Perplexity Eva
Page 17: ALP can be used to obtain prior pro
Page 21 and 22: abilities of n-grams are stored in
Page 23 and 24: main (static) n-gram model. As the
Page 25 and 26: There are many popular examples sho
Page 27 and 28: y Chen et al., who proposed a so-ca
Page 29 and 30: confusion among researchers, and ma
Page 31 and 32: language model took almost a week u
Page 33 and 34: w(t) s(t-1) s(t) U V W y(t) Figure
Page 35 and 36: ate is halved at start of every new
Page 37 and 38: or using matrix-vector notation as
Page 39 and 40: information for more than 5 time st
Page 41 and 42: A simple solution to the exploding
Page 43 and 44: output layer changes to computation
Page 45 and 46: While RNN models can overcome this
Page 47 and 48: complex or random architectures (su
Page 49 and 50: While for any of the previous point
Page 51 and 52: where λ is the interpolation weigh
Page 53 and 54: model with default SRILM cutoffs pr
Page 55 and 56: experiments, we have used the one i
Page 57 and 58: Perplexity (Penn corpus) 145 140 13
Page 59 and 60: with syntactical NNLMs would be pre
Page 61 and 62: Table 4.3: Combination of individua
Page 63 and 64: Table 4.6: Results on Penn Treebank
Page 65 and 66: 4.6 Conclusion of the Model Combina
Page 67 and 68: were: 400 classes, hidden layer siz
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Entropy per word on the WSJ test da
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Table 5.3: Results on the WSJ setup
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Table 5.5: Results for models <stro
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trained together with a maximum ent
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wt-3 wt-2 wt-1 D D D P(wt|context)
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n-gram probabilities. However, it w
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Table 6.1: Training corpora for NIS
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Perplexity 360 340 320 300 280 260
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Entropy per word 9 8.5 8 7.5 7 6.5
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1 a a a 1 2 3 P(w(t)|*) ONE TWO THR
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Table 6.4: Perplexity on the evalua
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Entropy reduction per word over KN4
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Table 6.6: Perplexity with the new
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Entropy reduction over KN5 -0.04 -0
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as a baseline, and 12.3% after resc
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Table 7.1: BLEU on IWSLT 2005 Machi
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Table 7.3: Size of compressed text
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Table 7.4: Accuracy of different la
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Table 7.6: Entropy on PTB with n-gr
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8.1 Machine Learning One possible d
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that almost every non-trivial compu
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supervision such as one digit at a
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Chapter 9 Conclusion and Future Wor
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from the expensive part of the mode
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Bibliography [1] A. Alexandrescu, K
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[23] D. Filimonov, M. Harper. A joi
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[50] T. Mikolov, S. Kombrink, L. Bu
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[77] W. Wang, M. Harper. The SuperA
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Test Phase After the model is train
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• compute sentence-level scores g
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Appendix B: Data generated from mod
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Appendix C: Example of decoded utte
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AND SAYS THIS SWING IS WITHIN A REA
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