to date by rescoring subword lattices using subword NNLMs, 27.1 % WER for spontaneous Finnish conversations, and 21.9 % WER for spontaneous Estonian conversations. This is the first published evaluation of subwords in conversational Finnish and Estonian speech recognition tasks.

Our conclusions are slightly different from those earlier published on standard Finnish and Estonian tasks, where n-gram models based on statistical morphs have provided a large improvement to speech recognition accuracy [@Siivola:2003; @Hirsimaki:2006; @Kurimo:2006]. An important reason is that we are able to use very large vocabularies (around two million words) in the word-based n-gram models. Recently it has been noticed that the gap between subword and word models becomes quite small when such a large word vocabulary is used [@Varjokallio:2016]. In our conversational Finnish and Estonian experiments, word and subword n-gram models performed quite similarly in terms of evaluation set word error rate. Our new observation is that neural networks are especially beneficial for modeling subwords—subword NNLMs are clearly better than word NNLMs trained using the full vocabulary.

Another approach for very large vocabulary speech recognition is using word classes in the language models. We evaluate different algorithms for clustering words into classes. Recent comparisons have shown an advantage 