Deferred NAM: Low-latency Top-K Context Injection via DeferredContext
  Encoding for Non-Streaming ASR
Zelin Wu Gan Song Christopher Li Pat Rondon Zhong Meng Xavier Velez Weiran Wang Diamantino Caseiro Golan Pundak Tsendsuren Munkhdalai Angad Chandorkar Rohit Prabhavalkar
Contextual biasing enables speech recognizers to transcribe important phrases
in the speaker's context, such as contact names, even if they are rare in, or
absent from, the training data. Attention-based biasing is a leading approach
which allows for full end-to-end cotraining of the recognizer and biasing
system and requires no separate inference-time components. Such biasers
typically consist of a context encoder; followed by a context filter which
narrows down the context to apply, improving per-step inference time; and,
finally, context application via cross attention. Though much work has gone
into optimizing per-frame performance, the context encoder is at least as
important: recognition cannot begin before context encoding ends. Here, we show
the lightweight phrase selection pass can be moved before context encoding,
resulting in a speedup of up to 16.1 times and enabling biasing to scale to 20K
phrases with a maximum pre-decoding delay under 33ms. With the addition of
phrase- and wordpiece-level cross-entropy losses, our technique also achieves
up to a 37.5% relative WER reduction over the baseline without the losses and
lightweight phrase selection pass.