Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription
Table 2: %WER of context dependent and independent phonetic and graphemic TDNN-LSTM models on dev17b.
['Model', 'Context', '%WER', 'RTF']
Graphemic systems are also expected to be sensitive to the choice of acoustic modelling context. Wider contexts should be more suitable for graphemic systems as they can better account for the mismatch between the orthographic and spoken form. However, shorter contexts are appealing due to their simplicity and speed of training as well as decoding. Though mono-grapheme units yield twice as large a degradation as mono-phone units, the simplicity of graphemic lexicons offers an interesting compromise.