What Is Automatic Speech Recognition and How Does It Work?
Blog post from Deepgram
Automatic Speech Recognition (ASR) technology, which converts spoken language into machine-readable text, has become an integral part of modern voice AI systems, seeing widespread use in industries such as healthcare, contact centers, and media. In 2026, four primary ASR architectures dominate: streaming Conformer models for real-time processing, on-device encoder-decoder transformers for privacy-sensitive applications, emerging unified Speech LLMs that aim to overcome traditional error accumulation in chained pipelines, and hybrid LLM post-processing systems. Despite advancements, challenges like racial and dialect bias, hallucination errors, and real-world noise degradation persist, highlighting the limitations of relying solely on Word Error Rate (WER) metrics for evaluating ASR performance. The market for ASR providers has expanded, with companies such as Deepgram, OpenAI, and ElevenLabs offering diverse solutions that cater to different needs, including privacy concerns and multilingual support. As ASR technology evolves, its impact on privacy-sensitive industries grows, but practical deployment requires testing against real-world audio to address specific use cases effectively.
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