Acoustic data sharing for Afghan and Persian languages

In this work, we compare several known approaches for multilingual acoustic modeling for three languages, Dari, Farsi and Pashto, which are of recent geo-political interest. We demonstrate that we can train a single multilingual acoustic model for these languages and achieve recognition accuracy close to that of monolingual (or language-dependent) models. When only a small amount of training data is available for each of these languages, the multilingual model may even outperform the monolingual ones. We also explore adapting the multilingual model to target language data, which are able to achieve improved automatic speech recognition (ASR) performance compared to the monolingual models for both large and small amounts of training data by 3% relative word error rate (WER).