Lower Frame Rate Neural Network Acoustic Models
Abstract
Recently neural network acoustic models trained with Connectionist
Temporal Classification (CTC) were proposed as an alternative approach
to conventional cross-entropy trained neural network acoustic models which output frame-level decisions every 10ms~\cite{senior15asru}. As opposed to
conventional models, CTC learns an alignment jointly with the acoustic
model, and outputs a \textit{blank} symbol in addition to the
regular acoustic state units. This allows the CTC model to run with a
lower frame rate, outputting decisions every 30ms rather than 10ms as
in conventional models, thus improving overall system latency. In this
work, we explore how conventional models behave with lower frame
rates. On a large vocabulary Voice Search task, we will show that with
conventional models, we can slow the frame rate to 40ms while improving WER by 3\% relative over a CTC-based model.
Temporal Classification (CTC) were proposed as an alternative approach
to conventional cross-entropy trained neural network acoustic models which output frame-level decisions every 10ms~\cite{senior15asru}. As opposed to
conventional models, CTC learns an alignment jointly with the acoustic
model, and outputs a \textit{blank} symbol in addition to the
regular acoustic state units. This allows the CTC model to run with a
lower frame rate, outputting decisions every 30ms rather than 10ms as
in conventional models, thus improving overall system latency. In this
work, we explore how conventional models behave with lower frame
rates. On a large vocabulary Voice Search task, we will show that with
conventional models, we can slow the frame rate to 40ms while improving WER by 3\% relative over a CTC-based model.
Research Areas
