ISCA Archive Interspeech 2024
ISCA Archive Interspeech 2024

Leveraging large language models for post-transcription correction in contact centers

Bramhendra Koilakuntla, Prajesh Rana, Paras Ahuja, Srikanth Konjeti, Jithendra Vepa

Contact centers depend on Automatic Speech Recognition (ASR) to power their downstream tasks. However, any mis-transcription in the ASR can have a significant impact on their downstream tasks. This issue is compounded by the extensive array of diverse brand and business names. Traditional transcription correction methods have a long development cycle and require skilled resources. Most of the time these errors will have a context, suggesting a search and replace solution in the post-call analytics platform. But identifying these contexts is time-consuming and tedious. Moreover, these words may get recognized in various similar forms, further complicating the situation. To tackle this, we propose a post transcription correction module by employing Large Language Models (LLMs) to detect these contexts, termed ‘anchors’ and to correct phonetically similar misrecognised words. By leveraging anchor phrases, we can pinpoint the error occurrences and correct the misrecognized.