  We discuss the problem of echographic transcription in autoregressive
sequence-to-sequence attentional architectures for automatic speech
recognition, where a model produces very long sequences of repetitive outputs
when presented with out-of-domain utterances. We decode audio from the British
National Corpus with an attentional encoder-decoder model trained solely on the
LibriSpeech corpus. We observe that there are many 5-second recordings that
produce more than 500 characters of decoding output (i.e. more than 100
characters per second). A frame-synchronous hybrid (DNN-HMM) model trained on
the same data does not produce these unusually long transcripts. These decoding
issues are reproducible in a speech transformer model from ESPnet, and to a
lesser extent in a self-attention CTC model, suggesting that these issues are
intrinsic to the use of the attention mechanism. We create a separate length
prediction model to predict the correct number of wordpieces in the output,
which allows us to identify and truncate problematic decoding results without
increasing word error rates on the LibriSpeech task.
