Multi-Graph Decoding for Code-Switching ASR
Table 1: Perplexities obtained on the Dutch component of the development and test transcriptions
['LM', 'Graph', 'Total # Dutch words', 'Dev.', 'Test']
The RNN-LMs with gated recurrent units (GRU) In total, we train 7 LMs : (1) baseline CS LM (cs) trained on bilingual text (107M), (2) baseline monolingual Frisian LM (fy) trained on 37M Frisian words, (3) baseline monolingual Dutch LM (nl) trained on 9M words, (4) larger monolingual Dutch LM (nl+) trained on 109M words, (5) largest monolingual Dutch LM (nl++) trained on 309M words, (6) nl+ interpolated with cs (interp-nl+) and (7) nl++ interpolated with cs (interp-nl++). For the last two LMs, the interpolation weights yielding the lowest perplexity on the transcriptions of the development set are used. These models are used in single-graph ASR systems to compare the performance with the corresponding multi-graph ASR system using the same amount of training Dutch text data. The perplexities of the CS and Dutch LMs on the Dutch component of the FAME!