Speaker 1: Ever wonder why AI systems convert speech to text and then back to speech instead of just sticking to voice? Well, we've got the answer for you. Now you might ask, why not just do everything in voice? The answer comes down to some practical and compelling reasons for leveraging this speech to text, followed by text processing, then text to speech. First and foremost, it's all about efficiency. Transforming speech into text, then processing it by converting it to numerical vectors or features for machine learning models, streamlines a process and speeds up AI's decision-making and predictions. Then comes the quality improvement. There is so much text data that we can train large language models on. So if we just take the voice and turn it into text, then we get all this quality benefits for free of all kinds of tasks we can do with it. Then all we have to do is map it back to voice. This three-step method, voice to text, text processing, and then text back to speech, sets the stage for seamless integration with other text-based applications in the middle step, enhancing the AI's versatility. The cherry on top is high quality tailored and controlled responses, thanks to large language models. Popular AI voice assistants like Siri, Google Assistant, and Amazon Alexa all utilize this speech-to-text and text-to-speech conversion process to better understand your spoken requests and provide accurate audible responses. Ultimately, by converting speech to text, processing in text, and then converting it back to speech again, AI developers craft systems that are more efficient, higher quality, and adaptable. A definite win-win situation for all. Microsoft Mechanics www.microsoft.com www.microsoft.com