What is ASR? A Comprehensive Overview of Automatic Speech Recognition Technology

Speech recognition, also known as automatic speech recognition (ASR), is the process of converting spoken language into written text by a machine or computer program. The goal of ASR technology is to achieve human-like accuracy and efficiency in transcribing speech accurately and quickly. Two main approaches are used in ASR: traditional hybrid approach and end-to-end deep learning approach. Traditional hybrid models involve separate acoustic, language, and pronunciation models trained independently with forced aligned data. In contrast, end-to-end deep learning models directly map sequences of input acoustic features into sequences of words without the need for force-aligned data or external models. End-to-end Deep Learning models have several advantages over traditional hybrid models, including higher accuracy, faster training times, and fewer requirements for specialized knowledge or human labor in model development. However, both approaches still face challenges related to achieving perfect accuracy levels due to factors such as dialects, slang, pitch variations, and other nuances in spoken language. ASR technology has many applications across various industries, including telephony (call tracking, cloud phone solutions, contact centers), video platforms (real-time and asynchronous video captioning), media monitoring (brand detection and topic analysis), virtual meetings (transcription and content analysis), and more. As the field continues to evolve, we can expect further advancements in ASR accuracy, efficiency, and integration into everyday life and industry applications.