3 research outputs found
Implementing contextual biasing in GPU decoder for online ASR
GPU decoding significantly accelerates the output of ASR predictions. While
GPUs are already being used for online ASR decoding, post-processing and
rescoring on GPUs have not been properly investigated yet. Rescoring with
available contextual information can considerably improve ASR predictions.
Previous studies have proven the viability of lattice rescoring in decoding and
biasing language model (LM) weights in offline and online CPU scenarios. In
real-time GPU decoding, partial recognition hypotheses are produced without
lattice generation, which makes the implementation of biasing more complex. The
paper proposes and describes an approach to integrate contextual biasing in
real-time GPU decoding while exploiting the standard Kaldi GPU decoder. Besides
the biasing of partial ASR predictions, our approach also permits dynamic
context switching allowing a flexible rescoring per each speech segment
directly on GPU. The code is publicly released and tested with open-sourced
test sets.Comment: Accepted to Interspeech 202
Knowledge-Based Word Lattice Rescoring in a Dynamic Context
Recent advances in automatic speech recognition (ASR) technology continue to be based heavily on data-driven methods, meaning that the full benefits of such research are often not enjoyed in domains for which there is little training data. Moreover, tractability is often an issue with these methods when conditioning for long-distance dependencies, entailing that many higher-level knowledge sources such as situational knowledge cannot be easily utilized in classification. This paper describes an effort to circumvent this problem by using dynamic contextual knowledge to rescore ASR lattice output using a dynamic weighted constraint satisfaction function. With this method, it was possible to achieve a roughly 80 % reduction in WER for ASR in the context of an air traffic control scenario. Index Terms: lattice rescoring, knowledge-based, contextsensitivity 1