Question

A telecommunications company wants to transcribe thousands of recorded customer support calls stored in Cloud Storage. The audio contains domain-specific product names and includes overlapping periods where multiple speakers talk. The team has no ML expertise and wants the fastest path to accurate transcripts that identify who said what, while boosting recognition of their product terminology. Which approach best meets these needs?

Accepted Answer

Use the Speech-to-Text API with speaker diarization enabled and provide speech adaptation (phrase boosting) for the product terminology.

Answer

Train a custom AutoML tabular model on transcribed samples to classify the audio segments by speaker and content.

Answer

Use the Natural Language API to process the audio files and extract speaker turns and entities directly.

Answer

Deploy an open-source Whisper model from Model Garden and fine-tune it on the full call archive before transcribing.