  Interactive speech recognition systems must generate words quickly while also
producing accurate results. Two-pass models excel at these requirements by
employing a first-pass decoder that quickly emits words, and a second-pass
decoder that requires more context but is more accurate. Previous work has
established that a deliberation network can be an effective second-pass model.
The model attends to two kinds of inputs at once: encoded audio frames and the
hypothesis text from the first-pass model. In this work, we explore using
transformer layers instead of long-short term memory (LSTM) layers for
deliberation rescoring. In transformer layers, we generalize the
"encoder-decoder" attention to attend to both encoded audio and first-pass text
hypotheses. The output context vectors are then combined by a merger layer.
Compared to LSTM-based deliberation, our best transformer deliberation achieves
7% relative word error rate improvements along with a 38% reduction in
computation. We also compare against non-deliberation transformer rescoring,
and find a 9% relative improvement.
