We use Speech-to-Text and Text-to-Speech to be able to talk to our users. We have an AI meaning engine that back-ends that. Once we get the speech, we can tell what it means. That's our use case. When we tested Speech-to-Text a few years ago, it was better than the equivalent IBM products because it was more accurate. But it's not by any means 100% accurate, and we have to correct for those errors in our AI software. It's using neural networks and that stochastic processing is 70-75% accurate. It gets it wrong too often, and we really don't like that. Since I personally work with this, I don't like that a lot. But they seem to be the best game in town right now. There's nobody else out there doing STT and TTS that is as good as them. There are several competitors trying, including Nuance and IBM, but their solutions are not very good, at least not as good as Google Cloud Text-to-Speech.
We use Google Cloud Text-to-Speech when we have an IVR and need to say something written on the API through the IVR. For example, if I'm on the IVR and need to say something to the customer that is written on the API, I need to vocalize this text. I use Google Cloud Text-to-Speech to take this text and vocalize it to the client.
The solution is used for developing translators for chatbots. In the past year, we won the hackathon with the chatbot and the chatbot is in two languages. We needed some solution for that translation. We didn't want to have two chatbots, one in English and one in Spanish. We wanted to have one chatbot in a multi-language format, and that is really a problem.
Text-To-Speech Services offer advanced solutions to convert text into human-like speech, enhancing accessibility and engagement across digital platforms.Comprising cutting-edge technologies, these services enable businesses to create audio content efficiently. They support a broad range of languages and voices, providing realistic speech synthesis suitable for various applications, from e-learning to customer service automation. These services integrate easily with existing systems and can be...
We use Speech-to-Text and Text-to-Speech to be able to talk to our users. We have an AI meaning engine that back-ends that. Once we get the speech, we can tell what it means. That's our use case. When we tested Speech-to-Text a few years ago, it was better than the equivalent IBM products because it was more accurate. But it's not by any means 100% accurate, and we have to correct for those errors in our AI software. It's using neural networks and that stochastic processing is 70-75% accurate. It gets it wrong too often, and we really don't like that. Since I personally work with this, I don't like that a lot. But they seem to be the best game in town right now. There's nobody else out there doing STT and TTS that is as good as them. There are several competitors trying, including Nuance and IBM, but their solutions are not very good, at least not as good as Google Cloud Text-to-Speech.
We use Google Cloud Text-to-Speech when we have an IVR and need to say something written on the API through the IVR. For example, if I'm on the IVR and need to say something to the customer that is written on the API, I need to vocalize this text. I use Google Cloud Text-to-Speech to take this text and vocalize it to the client.
The solution is used for developing translators for chatbots. In the past year, we won the hackathon with the chatbot and the chatbot is in two languages. We needed some solution for that translation. We didn't want to have two chatbots, one in English and one in Spanish. We wanted to have one chatbot in a multi-language format, and that is really a problem.