Amazon Transcribe vs. IBM Watson Text to Speech

Amazon Transcribe can be an excellent tool for businesses where being able to convert speech or audio to text, in a searchable and reportable form, would be useful. For a call center (inbound or outbound), the ability to have a rich transcription of each call (and being able to search it for keywords) is an incredibly valuable benefit. For business meetings, being able to turn a 60 or 90-minute call into a readable transcript to search or refresh yourself or others is a very large time saver which will help you work more efficiently. The software does offer many deeper integrations, such as being able to track script usage (for call centers) or interruptions, deviations, etc.. which would be very valuable to a management team and for training purposes.

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I advise Watson for all scenarios where you need AI to speak. I don't use this for this goal, but I suppose that could be a good solution for tourism and travel: companies in the hospitality industry can make it easier for people to
get around and offer tours in numerous languages, all at the same time. In telecommunications, could be used to create customized messaging that the caller can use with customers, and it can generate words from a customer’s records that are read to them in a professional and friendly voice. It's very good for English, for Italian is a little "robotic" but the pronunciation is right.

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• It converts live recordings to text with few errors.
• It has powerful speech recognition models- it transcribes well even low quality audios.

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• Improve customer experience and engagement.
• Offers both on-premise and cloud deployments.
• Automate requests and transactions in our agency.
• Voice recognition.
• Allows me to choose dialect which help select accent of the selected speaker.

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• It was not easy to bring Amazon Transcribe to life, but kudos to the vendor for the free support they offered.

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• Add sentiment variation.
• Add parameter in order to change the characteristic of the voice.
• Use custom voice in order to tune the speaker.

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I use Google Cloud Speech to Text and Amazon Transcribe. What makes Amazon Transcribe better for me is the accuracy of the audio-to-text conversion. I have found out that Amazone Transcribe is better at handling homophones, contractions, abbreviations, and acronyms. Another feature that makes Amazon Transcribe my No. 1 choice is its use of punctuation marks. I can also feed my own list of vocabulary into Amazon Transcribe to help me acquire better results.

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I've never been a fan of the robotic voice of text-to-speech tools and I've always thought they sounded flat and bland. Until I tried IBM Watson Text to Speech, that is. This service uses advanced deep learning techniques to synthesize speech output in natural-sounding voices. I was blown away by how good the voices were that I've been using it for personal stuff like journaling and brainstorming sessions. And I'm definitely going to be using this for podcasting and my YouTube channel.

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• Cost reductions in KYC automating our video approvals.
• Better customer service.
• A good base for building new products and services around speech to text.

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• Enhance rapid and convenient customer interaction.
• Client issues are easy to solve by allocating critical info in their native language.
• Automating requests and transactions reduce hold time which improves customer satisfaction.

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