Identify voice and speech with the latest AI analytics
With the rise of the Internet of Things, both voice recognition and speech recognition are becoming essential, particularly in finding ways to secure machine-to-machine networks. What can be confusing is that the term Voice Recognition is often used interchangeably with Speech Recognition. But the two have a different meaning and purpose.
Voice Recognition focuses on the voice. Just as every individual has a unique fingerprint, we each make different ‘voiceprints’ as a product of the unique combination of our physiology, personality and behavior patterns. Essential factors are the size and shape of the mouth, combined with pitch, speaking rate, inflection and accent. Voiceprints can be measured passively as a user speaks naturally in conversation, or actively, when a person is required to say a passphrase. This recognition may be broken into two categories authenticate the identity of a person: ‘Voice Verification’ and ‘Voice Identification.’ The former determines a match by cross-references speech patterns against a single pre-recorded sample, while the latter compares against multiple samples.
Speech Recognition is different as its focus is words. It is a user interface technology that strips away accent and personal idiosyncrasies to detect the words that are spoken by cross-referencing to a progressively complete database of an individual’s pre-recorded words. Smart phones users are well aware of speech recognition’s ability to control technology vocally, and that it improves with use. It is used to receive or interpret dictation, to follow spoken commands or to generate text and medical transcription. Speech recognition also allows the opportunity to speak with robots and is an especially useful communication tool for the hearing impaired.
This technology also brings accuracy in noise detection, which works by first converting sound waves into an electrical signal. Using a computer sound card and a microphone, advanced technologies process these signals. Discrete segments comprising of several tones are isolated and assembled as syllables and ‘words.’ Both speech recognition and noise detection are especially valuable in automatically triggering alerts and alarms.