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Google Cloud Speech-to-Text review
Principal Architect & NLP Python Developer at a computer software company with 1-10 employees
Support challenges persist despite audio technology advancements
Google Cloud Speech-to-Text is not entirely accurate, so we have to correct for those errors in our AI software. It uses neural networks, and that stochastic processing is 70% to 75% accurate. It gets it wrong too often, and since I personally work with this, I don't appreciate that. However, they seem to be the best option currently. We...
Google Cloud Speech-to-Text review
Software Developer at a consultancy with 11-50 employees
Speaker diarization enhances call review processes but improvements needed in...
The major challenge with Google Cloud Speech-to-Text is that not every call is clear. Our representative may be in a silent environment, but the client can be anywhere. We need to manage background noise for all calls, so handling audio file clarity is a challenge. Sometimes, speaker diarization is affected, leading to incorrect speaker...
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