Automated Speech Recognition (ASR), also known as speech-to-text (STT), is a versatile subfield of computational linguistics that configures technologies that enable computers to recognize and convert natural spoken language into text.
Specific ASR software integrations rely on programmers to initially ‘train’ the software to recognize speech by reading a series of texts and isolated vocabulary into the system.
As opposed to the “speaker dependent” systems that require training, ones that do not require such training are known as “speaker independent” systems.
To this day, the most advanced versions of ASR systems revolve around Natural Language Processing (NLP). By interlacing ASR with NLP technology, systems have a greater probability of answering its users more accurately by mimicking humans as closely as possible through natural language.
Nevertheless, despite weaving natural language processing technology into ASR systems to increase accuracy rates, perfect results can only truly be attained when humans create ideal dialogue conditions such as simple ‘yes’ and ‘no’ styled questions.
The flow under which automated speech recognition systems operate to break down and analyze text to respond in a significant way goes as follows:
The two central Automatic Speech Recognition software adaptations are: