Discussions

Automatic audio transcription technology has matured dramatically, evolving from experimental systems with limited accuracy into production-ready solutions that power enterprise-scale workflows. The ability to convert spoken content into searchable, analyzable text has become foundational to modern media management, research, and accessibility initiatives.

The landscape of automatic audio transcription encompasses multiple competing approaches, each with distinct strengths. Research comparing leading systems—including Google Cloud Speech-to-Text, Meta's wav2vec 2.0, NVIDIA's NeMo, and OpenAI's Whisper—reveals significant performance variations depending on audio characteristics and use cases . For survey research applications, some state-of-the-art systems now outperform commercial cloud offerings, highlighting the importance of matching transcription technology to specific requirements.