A technology that enables computers to interpret and convert spoken language into written text. It serves as the foundation for voice-driven applications, including virtual assistants, voice search, transcription services, and automated customer support. By transforming audio input into text, ASR allows users to interact with digital systems using natural speech instead of typed commands.
Modern ASR systems employ a combination of acoustic modeling, language modeling, and machine learning (ML) techniques, particularly deep learning (DL), to recognize speech patterns with high accuracy. These systems analyze audio waveforms, break them down into phonemes (the basic units of sound), and match them against a trained vocabulary to generate transcriptions.
ASR is utilized across various industries to streamline workflows, enhance accessibility, and improve user experiences. In healthcare, it facilitates clinical documentation; in legal fields, it supports deposition transcription; in customer service, it enables call summarization and sentiment analysis. Advances in neural network architectures and the availability of large-scale audio datasets continue to push ASR performance closer to human-level transcription.
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