LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR

• URL: http://arxiv.org/abs/2409.13514v1
• Date: Fri, 20 Sep 2024 13:53:37 GMT
• Title: LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR
• Authors: Iuliia Thorbecke, Juan Zuluaga-Gomez, Esaú Villatoro-Tello, Andres Carofilis, Shashi Kumar, Petr Motlicek, Karthik Pandia, Aravind Ganapathiraju,
• Abstract summary: We propose a light on-the-fly method to improve automatic speech recognition performance. We combine a bias list of named entities with a word-level n-gram language model with the shallow fusion approach based on the Aho-Corasick string matching algorithm. We achieve up to 21.6% relative improvement in the general word error rate with no practical difference in the inverse real-time factor.
• Score: 3.841280537264271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the recent success of end-to-end models for automatic speech recognition, recognizing special rare and out-of-vocabulary words, as well as fast domain adaptation with text, are still challenging. It often happens that biasing to the special entities leads to a degradation in the overall performance. We propose a light on-the-fly method to improve automatic speech recognition performance by combining a bias list of named entities with a word-level n-gram language model with the shallow fusion approach based on the Aho-Corasick string matching algorithm. The Aho-Corasick algorithm has proved to be more efficient than other methods and allows fast context adaptation. An n-gram language model is introduced as a graph with fail and output arcs, where the arc weights are adapted from the n-gram probabilities. The language model is used as an additional support to keyword biasing when the language model is combined with bias entities in a single context graph to take care of the overall performance. We demonstrate our findings on 4 languages, 2 public and 1 private datasets including performance on named entities and out-of-vocabulary entities. We achieve up to 21.6% relative improvement in the general word error rate with no practical difference in the inverse real-time factor.

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