What is STT?

An STT engine takes audio, extracts speech units from it and picks the most likely sequence of words given the language and context. Modern models work in streaming mode (for real-time dialogue) or in batch (for transcribing recordings). Accuracy depends on audio clarity, language and accent, industry vocabulary and whether speakers are separated by channel.

STT is the foundation of two big scenarios. In conversation analysis it produces the transcript on top of which diarization, metrics, topics and search work. In voice agents it turns the caller's speech into text in real time, which the language model then understands — without fast and accurate recognition, live dialogue is impossible.

Recognition engines differ in languages, accuracy, latency and cost, so different ones are chosen for a given task. For some scenarios minimal latency matters most, for others maximum accuracy or a low price at large volumes, and sometimes the ability to recognize locally without sending audio outside.

STT accuracy can also be influenced by processing techniques: separating speakers by channel, noise reduction, cutting silence before recognition. The reliability of all downstream analytics depends directly on the quality of this step, since recognition errors carry over into metrics, topics and extracted data.