Async transcription accuracy on hard audio: noisy call centers, overlapping speakers, and filler words

The text discusses the challenges and solutions for achieving high transcription accuracy with speech-to-text technologies in difficult audio environments, such as noisy call centers with overlapping speakers and filler words. It highlights that while most speech-to-text demos showcase clean audio, real-world scenarios often involve messy audio where the accuracy is truly tested. The Universal-3.5 Pro model is designed to handle such hard audio, providing significant improvements in entity accuracy, especially for critical tokens like email addresses and medical terms. Instead of pre-cleaning audio, server-side tools like Voice Focus are recommended to isolate primary speakers, and multichannel transcription is advised to prevent cross-channel bleed. Additionally, the use of keyterms prompting can help the model recognize specific domain vocabulary, while contextual prompting allows customization of how filler words and disfluencies are transcribed. The text emphasizes the importance of tuning transcription models against one's own challenging audio samples to ensure the best performance in practical applications.

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