  State-of-the-art English automatic speech recognition systems typically use
phonetic rather than graphemic lexicons. Graphemic systems are known to perform
less well for English as the mapping from the written form to the spoken form
is complicated. However, in recent years the representational power of
deep-learning based acoustic models has improved, raising interest in graphemic
acoustic models for English, due to the simplicity of generating the lexicon.
In this paper, phonetic and graphemic models are compared for an English
Multi-Genre Broadcast transcription task. A range of acoustic models based on
lattice-free MMI training are constructed using phonetic and graphemic
lexicons. For this task, it is found that having a long-span temporal history
reduces the difference in performance between the two forms of models. In
addition, system combination is examined, using parameter smoothing and
hypothesis combination. As the combination approaches become more complicated
the difference between the phonetic and graphemic systems further decreases.
Finally, for all configurations examined the combination of phonetic and
graphemic systems yields consistent gains.
