Speech-to-Text AI for Product Managers: How It Works and Key Considerations

This article discusses the functioning of Speech-to-Text AI, also known as Automatic Speech Recognition (ASR), and key considerations when selecting a suitable technology. Modern speech-to-text methods mainly involve End-to-End Deep Learning to route an acoustic waveform into a sequence of words. The accuracy of the transcriptions depends heavily on the training provided to the AI model with large amounts of data. Key aspects to consider include near human-level accuracy, features like automatic punctuation and speaker diarization, noise robustness, confidence scores, language support, consistent innovation, scalability, and security measures. The choice between free or paid plans is also crucial in determining the best fit for a business's needs.

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