A topic dependent class (TDC) language model (LM) is a topic-based LM that uses a semantic extraction method to reveal latent topic information from noun-document relation. Then a clustering for a given context is performed to define topics. Finally, a fixed window of word history is observed to decide the topic of the current event through voting in online manner. Previously, we have shown that TDC overperforms several state-of-the-art baselines in terms of perplexity. In this paper we evaluate TDC on automatic speech recognition experiment (ASR) for rescoring task. Experiments on read speech Wall Street Journal (English ASR system) and Mainichi Shimbun (Japanese ASR system) show that TDC LM improves both perplexity and word-error-rate (WER). The result shows that the proposed model gives improvements 3.0% relative on perplexity and 15.2% relative on WER for English ASR system, and 16.4% relative on perplexity and 24.3% relative on WER for Japanese ASR system.