Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts

Understand

For a multilingual podcast streaming service, it is critical to be able to deliver relevant content to all users independent of language.

• Podcast content relevance is conventionally determined using various metadata sources.
• However, with the increasing quality of speech recognition in many languages, utilizing automatic transcriptions to provide better content recommendations becomes possible.
• In this work, we explore the robustness of a Latent Dirichlet Allocation topic model when applied to transcripts created by an automatic speech recognition engine.