When being able to do everything with the touch of a keypad has become the norm, voice technology has just set a new benchmark. With digital voice assistants like Amazon’s Alexa, Google’s Assistant, Microsoft’s Cortana, and Apple’s Siri, we can do things we could only dream of before.
“Alexa, wake me up at 6 o’clock” or “Alexa, switch off the light.” Like a genie from a fairy tale, these voice assistants respond to our voice commands as soon as we say them.
How do these digital voice assistants work technically?
Smartphones, tablets, and other internet-connected devices use digital voice assistants. They start working as soon as a voice command is given.
The question is, “How?” At different points, state-of-the-art technologies like speech recognition, natural language processing, machine learning, and cloud-based technologies make this possible.
Natural Language Generation and Processing
Natural language processing (NLP) is when a computer can “read” or “understand” what a person says or writes, for example. The term “natural language generation” (NLG) refers to a machine’s ability to write or speak content in a way that humans can understand.
Machine Learning in Digital Voice Assistant
The Work Process of Digital Voice Assistants
Speech to text
The first step is “speech to text”. As soon as you tell your digital voice assistant, “Play music,” it knows what you want.
Speech recognition is the process of putting what you say into text. Every user will have a different accent, tone, and way of speaking. Voice assistants have very advanced speech recognition technology that can handle all these different ways of speaking. This is possible because of linguistic and semantic analysis.
Good “speech to text” software like Apple Dictation, Google Docs voice typing, and Dragon naturally speaking adjusts for background noise and differences in voice tone, pitch, and accent to provide accurate translation in multiple languages. The software looks at words like “eight” and “ate” that sound the same but are spelled differently. The speech-to-text software looks at the sentence’s context and syntax to find the best text match for the word you said. The software then matches the words it has analyzed with the text that best fits what you said.
Natural Language Processing
Once the speech has been turned into text, then it’s time to figure out its meaning. Natural Language Processing, or NLP, comes to the rescue here. NLP helps turn text that isn’t organized into text that is organized. so that a gadget can understand it. NLP matches the words (or commands) that people say with the right intent.
There are different NLP models, each of which can handle different kinds of speech in different ways. These voice assistants can figure out what words are being asked for and where they are in the request.
The command’s intent and the values of its slots are put into a well-organized data structure. Then it is sent to a cloud-based service that can handle different intents.
For example, if you say “Tell me about India” in a normal conversation, what should the Digital VI think you really want to say? Are you looking for the most recent news about India, flights to India, or the culture in India? Web search engines get around this problem by putting the answers to the “query” in decreasing order of what they think the person was looking for.
Every time Alexa or Siri makes a mistake when answering your question, it uses the information it gets from how it answered the question the first time to get better the next time. ML uses that information to improve, if a mistake is made. If the answer was good, the system also keeps track of that.
The rapid growth of digital voice assistants is due to data and machine learning. They keep getting better as they have more experience and collect more data.
Intent to Action
Intent to action, the last step, is meant to meet the user’s needs.
The information that is retrieved is in text form. It needs to be turned into cloud services that allow voice interfaces that use a list of pre-recorded words to say the results.
These services also use a mark-up language made just for speech synthesis to make it sound more like real speech. This mark-up language makes it possible to use emphasis, tone, pitch, sound, and words that are common in a certain area.
Most digital VIs are moving from answering simple questions (like what the weather is) to actually doing things. They can operate various electronics as they are built into cars, refrigerators, thermostats, light bulbs, and door locks.
You can find digital assistants in your office, at home, in your car, at a hotel, on your phone, and in many other places. They watch and collect data in real time and can pull information from different sources, like smart devices and cloud services, to make sense of what’s going on.
Digital assistants either keep recording information all the time. They wait for a word to “wake up” or “turn them on”. They don’t just collect information about the owner or authorized users. Personal digital assistants can collect and use personal information from people who haven’t given permission to do so, like their voices.
Most of the information that these digital assistants collect and use is personal. They could be used to identify the user, and could be sensitive. Did the digital assistant use all of that information to feed the algorithms or to get into the privacy of the guest?
People who are aware of and concerned about privacy tend to limit their use of digital tools. People who are less likely to protect their privacy use personal assistants a lot.