data.

Modern automatic speech recognition (ASR) systems can handle vocabularies as large as millions of words with simple n-gram language models, but a second recognition pass with a neural network language model (NNLM) is now necessary for achieving state-of-the-art performance. Vocabulary size is much more critical in NNLMs, as neural networks take a long time to train, and training and inference times depend heavily on the vocabulary size. While computational efficiency is the most important reason for finding alternatives to word-based modeling, words may not be the best choice of language modeling unit with regard to model performance either, especially when modeling agglutinative languages.

Subword models have been successfully used in Finnish ASR for more than a decade [@Hirsimaki:2006]. In addition to reducing the complexity of the language model, subword models bring the benefit that even words that do not occur in the training data can be predicted. However, subwords have not been used for modeling *conversational* Finnish or Estonian before. Our earlier attempts to use subwords for conversational Finnish ASR failed to improve over word models. In this paper, we show how subword models can be used in the FST-based Kaldi speech recognition toolkit and obtain the best results 