Speech and voice AI means AI models that work with spoken language. They turn speech into text, produce a spoken reply, or change a voice while a person is speaking, and they do it fast enough for a live conversation. They are the models inside car voice assistants, phone bots, smart speakers and dubbing tools.
There are three main jobs. The first is speech recognition: turning speech into text, which a speech-to-text (ASR) model does. The page on speech recognition explains the idea. The second is speech synthesis, also called text-to-speech: turning text into a natural voice. The third is changing a voice while someone speaks, for example its accent, as in accent conversion.
A voice assistant usually chains these jobs together. The caller speaks. A speech-to-text model writes down the words. A large language model (LLM) decides the reply. A text-to-speech model turns the reply into sound. Some newer models do all of this in one step, directly from speech to speech.
The hardest requirement is speed. In a conversation between two people, the gap before a reply is usually well under a second. If a bot takes two or three seconds, callers think the line is dead, or start speaking again. So every model in the chain must start its work before the previous one has finished. The speech-to-text model sends words on as they are heard. The text-to-speech model starts producing the sound of the first words while the rest of the reply is still being written. Real-time voice AI is built for exactly this.
Consider a multi-speciality hospital in Nagpur that receives 1,500 calls a day to book appointments, mostly in Marathi and Hindi. A voice bot answers each call. A patient says, in Marathi, that she needs to see a bone specialist tomorrow. The speech model turns her words into text in about a third of a second. The language model checks the free slots in the hospital's booking system. The bot then replies in Marathi that Dr Patil has a slot at 11 tomorrow morning. The reply begins less than a second after she stops speaking.
Voice models are sold to the makers of voice bots, contact centre software and conversational AI platforms. They learn from thousands of hours of recorded speech, transcribed word by word through data labelling. Accents, background noise and the mixing of two languages in one sentence, which is common in India, are what make these models hard to build.