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10 Voice Control Terms You Need to Know

 

Voice control is now becoming a popular interface with hands-free capabilities making daily tasks easier and quicker. How exactly does this innovative technology work to magically respond to your client’s every command? Here are 16 voice control keywords that will help explain how it all works.

 

1. Far-Field Microphones

 

Personal computing devices have had microphones for a long time, but they don’t work well from far away. Far-field microphones, on the other hand, are an array of mics that utilize their location in space to amplify and reduce signals. This makes it possible to speak from across the room in a “hands-free” environment. By suppressing certain surrounding noises in the environment, these microphones utilize algorithms to help deliver a clear and easily understandable signal. The far-field voice experience is enhanced by other technologies, defined below, which include barge-in, beamforming, noise reduction, acoustic cancellation, and automatic speech recognition. Because this array utilizes the distance between microphones in its calculations, it’s hard to make these devices smaller than a minimum threshold.

 

2. Barge-In

 

Imagine playing music or watching TV with a nearby far-field microphone. Trying to yell over the noise can be quite difficult. This is where “barge-in” technology comes in. With “barge-in,” the listening microphone is aware of the audio source and able to digitally remove it, thus reducing noise and increasing accuracy. Amazon Echo is a great example of this technology. 

 

3. Beamforming

 

Imagine you have a far-field microphone in a room with a TV on one side and you on the other. Even if the TV is relatively loud, beamforming technology enables the microphones to amplify your speech and reduce the noise from the TV, effectively making it easy to be heard in a loud environment. This is particularly useful in automotive applications where the driver is always in a fixed location and noise in front of the car can be reduced.

 

4. Microphone Array

 

We’ve mentioned this term a couple times, but it’s important to define as a standalone term. A microphone array is a single piece of hardware with multiple individual microphones operating in tandem. This increases voice accuracy with the ability to accept sounds from multiple directions regardless of background noise, the position of the microphone, and the speaker placement.

 

5. Automatic Speech Recognition

 

Often abbreviated as (ASR), it is the conversion of spoken language into written text. When you say “Hey Siri” and follow with “…send a text,” you’re watching ASR in action. In other words, “speech rec” (as it’s sometimes shortened) makes it possible for computers to know what you’re saying.

 

6. Speaker Recognition

 

Although easy to confuse with SR, speaker recognition is the specific art of determining who is speaking. This is achieved based on the characteristics of voices and a variety of technologies including Markov models, pattern recognition algorithms, and neural networks (defined below). Another term you might hear related to speaker recognition is “Voice Biometrics,” which defines the technology behind speaker recognition. There are two major applications of speaker recognition: 1) verification, which aims to verify if the speaker is who they claim, and 2) identification, the task of determining an unknown speaker’s identity.

 

7. Markov Models

 

Rooted in probability theory, a Markov Model uses randomly changing systems to forecast future states. A great example is the predictive text you’ve probably seen in your iPhone. If you type “I love,” the system can predict the next word to be “you” based on probability. There are four types of Markov models, including hidden and Markov chains. Markov Models are very important in speech recognition because it’s similar to how humans process text. The sentences “make the lights red” and “make the lights read” are pronounced the same, but understanding the probability helps assure accurate speech recognition.

 

8. Pattern Recognition

 

As the name suggests, this is a branch of machine learning that utilizes patterns and regularities in data to train systems. There’s a lot to pattern rec, with algorithms aiding in classification, clustering, learning, predicting, regression, sequencing, and more. Pattern recognition is very important in the field of speech recognition and understanding what sounds form what words.

 

9. Artificial Neural Networks

 

A computer system modeled on how we believe the human brain works, neural networks utilize artificial neurons to learn how to solve problems that typical rule-based systems struggle with. For example, neural networks are imperative for facial recognition, self-driving cars, and of course, voice control.

 

10. Natural Language Processing (NLP)

 

When a computer can analyze, understand, and derive meaning from human language, it is utilizing natural language processing. NLP covers a range of applications including syntax, semantics, discourse, and speech.