Building custom language models to supercharge speech-to-text performance for Amazon Transcribe

Amazon Transcribe is a fully-managed automatic speech recognition service (ASR) that makes it easy to add speech-to-text capabilities to voice-enabled applications. As our service grows, so does the diversity of our customer base, which now spans domains such as insurance, finance, law, real estate, media, hospitality, and more. Naturally, customers in different market segments have asked Amazon Transcribe for more customization options to further enhance transcription performance. We're excited to introduce Custom Language Models (CLM). The new feature allows you to submit a corpus of text data to train custom language models that target domain-specific use cases. Using CLM is easy because it capitalizes on existing data that you already possess (such as marketing assets, website content, and training manuals). In this post, we show you how to best use your available data to train a custom language model tailored for your speech-to-text use case. Although our walkthrough uses a transcription example from the video gaming industry, you can use CLM to enhance custom speech recognition for any domain of your choosing. This post assumes that you're already familiar with how to use Amazon Transcribe, and focuses on demonstrating how to use the new CLM feature.