  One of the most difficult speech recognition tasks is accurate recognition of
human to human communication. Advances in deep learning over the last few years
have produced major speech recognition improvements on the representative
Switchboard conversational corpus. Word error rates that just a few years ago
were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now
believed to be within striking range of human performance. This then raises two
issues - what IS human performance, and how far down can we still drive speech
recognition error rates? A recent paper by Microsoft suggests that we have
already achieved human performance. In trying to verify this statement, we
performed an independent set of human performance measurements on two
conversational tasks and found that human performance may be considerably
better than what was earlier reported, giving the community a significantly
harder goal to achieve. We also report on our own efforts in this area,
presenting a set of acoustic and language modeling techniques that lowered the
word error rate of our own English conversational telephone LVCSR system to the
level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000
evaluation, which - at least at the writing of this paper - is a new
performance milestone (albeit not at what we measure to be human performance!).
On the acoustic side, we use a score fusion of three models: one LSTM with
multiple feature inputs, a second LSTM trained with speaker-adversarial
multi-task learning and a third residual net (ResNet) with 25 convolutional
layers and time-dilated convolutions. On the language modeling side, we use
word and character LSTMs and convolutional WaveNet-style language models.
