Phoneme Level Language Models for Sequence Based Low Resource ASR
Table 2: PLM (Small and Large) and Multi-PLM (Large) perplexities for different languages in the training set.
['[EMPTY]', 'PLM Small', 'PLM Large', 'Multi-PLM Large']
It shows that the Multi-PLM model matches the performance of the PLM Large model while using roughly 6 times lesser parameters. For comparison, we also show the perplexities obtained using a smaller PLM model which uses roughly 1/6 effective trainable parameters (obtained by using LSTM with 256 units instead of 1024 and a dropout of 0 instead of 0.4). We can see that the Multi-PLM does better than the PLM Small model and showing that the model is benefiting from learning a shared representation.