Amazon Transcribe's custom vocabulary feature is the designated solution for improving transcription accuracy for domain-specific terms, such as product names with uncommon spellings. This feature allows developers to provide a list of specific words and their pronunciations to the ASR engine. The vocabulary can be quickly updated and redeployed as developers test and refine the workflow. This method directly addresses the need for rapid customization iterations to improve recognition of specific terms without the overhead and time required for full model retraining.

A. Amazon Lex is designed for building conversational interfaces (bots), not as a primary transcription service for analyzing pre-recorded audio like voicemails.

B. Fine-tuning a foundation model with Amazon Bedrock is a form of model retraining, which the scenario explicitly seeks to avoid for rapid, lightweight updates.

C. Amazon Kendra is an enterprise search service used to index and query documents; it does not directly participate in or improve the core ASR transcription process.

1. AWS Documentation - Amazon Transcribe Developer Guide: "Custom vocabularies". This guide states, "A custom vocabulary is a list of specific words that you want Amazon Transcribe to recognize in your audio input. These are generally domain-specific words and phrases, such as product names... that are not in the base vocabulary." This directly supports using custom vocabularies for product names. The guide also details the UpdateVocabulary API call, which facilitates the required frequent updates. (Source: AWS Documentation, Amazon Transcribe Developer Guide, Section: "Custom vocabularies").

2. AWS Machine Learning Blog: "Improving domain-specific transcription accuracy with Amazon Transcribe custom vocabularies". This official blog post explains, "Custom vocabularies enable you to provide a list of out-of-vocabulary (OOV) words... to Amazon Transcribe. This helps improve the accuracy of the speech recognition for these specific words." This confirms the feature's purpose and its effectiveness for the scenario's requirements. (Source: AWS Machine Learning Blog, May 21, 2020).

3. AWS Documentation - Amazon Lex V2 Developer Guide: "What Is Amazon Lex V2?". The documentation defines Lex as "a service for building conversational interfaces into any application using voice and text," confirming it is not the appropriate tool for a pure transcription task. (Source: AWS Documentation, Amazon Lex V2 Developer Guide, Section: "What Is Amazon Lex V2?").