1. A Survey of ICASSP 2013 Language Model Department of Computer Science & Information Engineering National Taiwan Normal University 報告者：郝柏翰 2013/06/19

2. Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition Ebru Arısoy et al., IBM T.J. Watson Research Center, NY

3. Introduction In this work, we propose an approximate method for converting a feedforward NNLM into a back-off n-gram language model that can be used directly in existing LVCSR decoders. We convert NNLMs of increasing order to pruned back- off language models, using lower-order models to constrain the n-grams allowed in higher-order models. 3

4. Method 4 In this paper, we propose an approximate method for converting a feedforward NNLM into a back-off language model that can be directly used in existing state-of-the-art decoders.

5. Method 5

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7. Experiments 7 More smooth Before After

8. Use of Latent Words Language Models in ASR: a Sampling-Based Implementation Ryo Masumura et al., NTT Media Intelligence Laboratories, Japan

9. Introduction This paper applies the latent words language model (LWLM) to automatic speech recognition (ASR). LWLMs are trained taking into account related words, i.e., grouping of similar words in terms of meaning and syntactic role. In addition, this paper also describes an approximation method of the LWLM for ASR, in which words are randomly sampled on the LWLM and then a standard word n-gram language model is trained. 9

10. Method 10 Latent Words Language Model –LWLMs are generative models with a latent variable for every observed word in a text.

11. Method 11

12. Expriments 12 This result shows that we can construct LWLM comparable to HPYLM if we generate sufficient text data. Moreover, highest performance was achieved with LWLM+HPYLM. This results shows that LWLM possesses properties different from those of the HPYLM, and further improvement is achieved if they are combined.

13. Incorporating Semantic Information to Selection of WEB Texts for Language Model of Spoken Dialogue System Koichiro Yoshino et al., Kyoto University, Japan

14. Introduction A novel text selection approach for training a language model (LM) with Web texts is proposed for automatic speech recognition (ASR) of spoken dialogue systems. Compared to the conventional approach based on perplexity criterion, the proposed approach introduces a semantic-level relevance measure with the back-end knowledge base used in the dialogue system. We focus on the predicate-argument (P-A) structure characteristic to the domain in order to filter semantically relevant sentences in the domain. 14

15. Method 15 Selection Based on Semantic Relevance Measure where C(.) stands for an occurrence count and P(D) is a normalization factor determined by the size of D. γ is a smoothing factor estimated with a Dirichlet prior

16. Method 16

17. Experiments 17