What is Automatic Speech Recognition?

What is Automatic Speech Recognition?

Automatic speech recognition (ASR) can be defined as the independent, computer‐driven transcription of spoken language into readable text in real time. In a nutshell, ASR is technology that allows a computer to identify the words that a person speaks into a microphone or telephone and convert it to written text.

Having a machine to understand fluently spoken speech has driven speech research for more than 50 years. Although ASR technology is not yet at the point where machines understand all speech, in any acoustic environment, or by any person, it is used on a day‐to‐day basis in a number of applications and services.

The ultimate goal of ASR research is to allow a computer to recognize in real‐time, with 100% accuracy, all words that are intelligibly spoken by any person, independent of vocabulary size, noise, speaker characteristics or accent.  Today, if the system is trained to learn an individual speaker's voice, then much larger vocabularies are possible and accuracy can be greater than 90%.

Commercially available ASR systems usually require only a short period of speaker training and may successfully capture continuous speech with a large vocabulary at normal pace with a very high accuracy. Most commercial companies claim that recognition software can achieve between 98% to 99% accuracy if operated under optimal conditions. `Optimal conditions' usually assume that users: have speech characteristics which match the training data, can achieve proper speaker adaptation, and work in a clean noise environment.

This explains why some users, especially those whose speech is heavily accented, might achieve recognition rates much lower than expected.

How Does ASR Work?

The goal of an ASR system is to accurately and efficiently convert a speech signal into a text message transcription of the spoken words independent of the speaker, environment or the device used to record the speech (i.e. the microphone).

This process begins when a speaker decides what to say and actually speaks a sentence. (This is a sequence of words possibly with pauses, uh’s, and um’s.) The software then produces a speech wave form, which embodies the words of the sentence as well as the extraneous sounds and pauses in the spoken input. Next, the software attempts to decode the speech into the best estimate of the sentence. First it converts the speech signal into a sequence of vectors which are measured throughout the duration of the speech signal. Then, using a syntactic decoder it generates a valid sequence of representations.