Automatic Speech Recognition (ASR): how speech-to-text models work and which one to use

Automatic speech recognition, or speech-to-text, converts spoken audio into written language by combining an audio encoder that interprets sound with a language model that generates coherent text. Modern systems are generally grouped into encoder-decoder, CTC, encoder-transducer, continuous-input speech LLM, and discrete-input speech LLM architectures, each differing in how audio and language components interact and in their trade-offs between accuracy, latency, streaming capability, scalability, and flexibility. Models such as Wav2Vec2, Whisper, Kyutai-STT, and NVIDIA’s Nemotron-Speech-Streaming illustrate these approaches, from self-supervised CTC systems and robust large-scale encoder-decoders to low-latency streaming designs. The discussion emphasizes that no model is universally best: organizations should evaluate candidates using their own audio, languages, accents, noise conditions, real-time requirements, deployment preferences, and total operating costs rather than relying solely on public benchmark scores such as word error rate.

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