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ASR

Speech recognition (SR) is the inter-disciplinary sub-field of computational linguistics which incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields to develop methodologies and technologies that enables the recognition and translation of spoken language into text by computers and computerized devices such as those categorized as smart technologies and robotics. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or just “speech to text” (STT).

Some SR systems use “training” (also called “enrollment”) where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person’s specific voice and uses it to fine-tune the recognition of that person’s speech, resulting in increased accuracy. Systems that do not use training are called “speaker independent”[1] systems. Systems that use training are called “speaker dependent”.

Speech recognition applications include voice user interfaces such as voice dialing (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[2][3][4] or speaker identification[5][6] 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 a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems. These speech industry players include Google, Microsoft, Hewlett Packard Enterprise,[7] IBM, Baidu (China), Apple, Amazon, Nuance, IflyTek (China), CDAC Mumbai many of which have publicized the core technology in their speech recognition systems as being based on deep learning.

 

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