Speech-to-text (STT) technology transforms spoken language into written text through several key steps including audio input, signal processing, phoneme recognition, language modeling, and text output. Recent advancements in deep learning and large language models have dramatically improved the accuracy and efficiency of STT systems, making them highly accurate and adaptable. STT is widely accessible through user-friendly online platforms and applications, benefiting individuals with disabilities, professionals, and everyday users alike. In contrast, text-to-speech (TTS) technology converts written text into natural-sounding spoken language using advanced artificial intelligence, featuring stages such as text analysis, linguistic processing, speech synthesis, and speech rendering. TTS systems rely heavily on natural language processing and can customize the voice by altering pitch, speed, and volume. Both STT and TTS have a wide range of practical applications across various fields, including transcription and dictation, voice assistants, accessibility, voice-controlled computing, education, language learning, media and entertainment, customer service, and more. While both technologies offer significant benefits, they also come with limitations such as accuracy concerns, privacy issues, and cost-effectiveness. The future of voice AI is incredibly promising, with ongoing research and development pushing the boundaries of what these technologies can achieve, particularly with the emergence of speech-language models that combine STT and TTS capabilities into a single unified system.