Speech recognition is a transformative technology that enables computers to understand and interpret spoken language, fostering seamless interaction between humans and machines. By implementing algorithms and machine learning techniques, speech recognition systems transcribe spoken words into text, facilitating a diverse array of applications. A speech recognition system must distinguish meaningful language from variations in accent, speed, pitch, background noise, microphone quality, and speaking style. Traditional automatic speech recognition (ASR) addressed this problem through separate components for acoustic modeling, pronunciation, and language modeling; it converts spoken language into written text. These systems were effective, but they depended heavily on expert-designed features and carefully constructed processing pipelines. Deep learning changed this approach by allowing models to learn complex representations directly from large collections of speech and text. Modern end-to-end systems can learn a direct mapping from an audio signal to a sequence of characters, subwords, or words. This approach has produced major gains in accuracy and made voice assistants, automated captions, transcription services, and hands-free interfaces widely available. The central challenge is not merely to recognize clean, familiar speech, but to build systems that remain accurate, fair, efficient, and trustworthy in the open world. Automatic speech recognition has become one of deep learning’s clearest practical successes. This paper provides an overview of deep learning and its applications in speech recognition tasks.
Deep learning, DL, deep machine learning, speech recognition, automatic speech recognition, ASR.
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