Speech recognition

Speech recognition

In computer science and electrical engineering, speech recognition (SR) is the translation of spoken words into text. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or just “speech to text” (STT). Some SR systems use “speaker-independent speech recognition” while others use “training” where an individual speaker reads sections of text into the SR system. These systems analyze the person’s specific voice and use it to fine-tune the recognition of that person’s speech, resulting in more accurate transcription. Systems that do not use training are called “speaker-independent” systems. Systems that use training are called “speaker-dependent” systems. Speech recognition applications include voice user interfaces such as voice dialling (e.g. “Call home”), call routing (e.g. “I would like to make a collect call”), domotic appliance control, search (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), speech-to-text processing (e.g., word processors or emails), and aircraft (usually termed Direct Voice Input). The term voice recognition or speaker identification refers to identifying the speaker, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person’s voice or it can be used to authenticate or verify the identity of a speaker as part of a security process. From the technology perspective, speech recognition has been going through several waves of major innovations since over some 50 years ago. The most recent wave of innovations since 2009, arguably the most important one which defines the current state of the art in speech recognition accuracy and has been in dominant use since 2013 throughout the speech industry worldwide, is based on deep learning concepts, architectures, methodologies, algorithms, and practical system implementations enabled by big training data and by GPU-based big compute.

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