Many areas of industry and ‘smart’ household advances are now adopting a variety of uses for speech recognition. In the last twenty years, there has been an upsurge in both academic papers and white papers published on the advancement of the technology. As big data, machine learning, and deep learning have advanced, so has speech recognition.
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There has been a long history of trial and error with several significant waves of innovations in speech recognition technology. Over time, the system actually improves, resulting in increased accuracy. The system uses machine learning methods to analyze the specific voice and speech patterns and uses that data to fine-tune its recognition of that person’s speech. When a system is undergoing enrollment, an individual speaker reads text or isolated vocabulary into the system. Training a speech recognition system is also known as ‘enrollment.’ Many types of speech recognition systems require ‘training.’ Like many forms of AI, training helps computer systems with their perception or recognition through the development of patterns. The science integrates knowledge and research in computer science, linguistics, and computer engineering. It is also sometimes referred to as Speech to Text (STT). Speech Recognition TechnologiesĪs a cross-disciplinary subfield of computer science and computational linguistics, automatic speech recognition (ASR) formulates methodologies and technologies that allow for the recognition and translation of spoken languages into text. Transcription can also be applied to any audio recordings, 911 calls, call centers recordings and others. This can be done with automatic speech recognition technology, a human transcriptionist, or the best of all, combining the two. The process of converting the speech in a video to text is called video transcription.
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