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 convert written content into spoken word, enhancing accessibility and user engagement for content creators. These tools are crucial for businesses aiming to reach auditory learners and those with reading difficulties.Text-To-Speech Services provide an efficient way to create natural-sounding audio outputs using advanced AI-driven algorithms. They are widely used in applications like virtual assistants, customer support, and educational tools, making digital content...
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.