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Know-How Conversational AI is Making Amazon Alexa Smarter

By Milan Jain Published in Artificial Intelligence 10-15 mins

Conversational AI - The Magic Behind Seamless Voice Control Experience of Alexa

Amazon Alexa has become a cute family member of every alternative Home. Everything can be controlled through Amazon Alexa, from switching on the light when you come back from the office to cleaning your Home via a robotic vacuum cleaner (while you are busy with some other work). But who made Alexa so bright!!!. Well, it's all about ' Conversational AI.' Conversion AI and Alexa are making a super-intelligent combination.

IBM was the first to invent the speech recognition machine in 1962. Back then, only a few could understand what speech recognition could do. IBM shoebox was one of the computers that recognized 16 spoken words and even digits from 1 - 9. Then came Clippy from Microsoft, and later voice-activated commands came along with the in-car system.

With this timeline, in 2011, Apple's Siri was invented. Later on, in 2012, Google introduced voice assistance. After several years, Amazon introduced Alexa. All of these are voice control services that respond like a human. However, the most sold voice-controlled device is Alexa due to its budget-friendliness, which has been integrated with Amazon Echo (smart speakers) and shows (intelligent speakers with display).

Amazon has been working with _neural networks, the most advanced state of conversational AI, to make Alexa sound like a human. Rohit Prasad, the senior vice president of Alexa at Amazon, said that adding neural networks helped AmazonAlexa grow in many ways.

Before, Alexa's algorithm was used to break down language into word parts or vocal sounds and then tried to string them together as seamlessly as possible. But it sounded chippy and robotic. So, Amazon Alexa works on neural networks which generate the whole text (text stemming in NLP) in real-time, which sounds more like a human.

An AI bot greeting hello on a tablet display. This represents coversational AI technology

What is conversational AI?

Conversational AI is how the interaction between computers and humans started. Other than menus, mouse clicks, and touchscreens, using our voice and having a conversation is one of the natural ways to communicate with the computer. Simply say it, and it will take care of the rest for you.

The new advancements in human-computer interaction make powerful computer features easy and more accessible. I.e., rather than making a lot of scrolls and clicking to play music, today you can easily say, "Alexa, play today's top songs."

A Conversational AI system is how people interact with computers. Conversational AI such a technology that controls a voice-based device like Amazon Echo. The conversations with such devices are like magic, and the developers have worked on this for years. With a Voice User Interface (VUI), voice solutions like Amazon Alexa can interact with humans much more effortlessly, solve problems and get smarter whenever you train them with custom skills.

What is Amazon Alexa?

What exactly is Alexa? Alexa is the Amazon cloud-based service for voice and is available on more than 100 million Amazon and third-party devices. With Alexa, you can create natural voice experiences for customers, offering them various methods to engage with tech and use it every day.

An amazon alexa eco dot 2nd gen device controlled by alexa voice services (AVS)

Alexa voice service (AVS) created by Amazon replicating real-life conversations. Alexa captures your voice whenever you ask, "What's the weather likely to look like tomorrow? The recording is transmitted over the internet and then transferred it is sent to Amazon's Alexa Voice Services, which transforms it into commands, and is then able to recognize it. The system transmits the right output on your phone. When you inquire with Alexa about weather conditions, her response is provided in the form of an audio stream.

Alexa shares the weather prediction without you having any communication with the system. It was clear the moment you drop connectivity to the internet, Alexa ceases to function.

Alexa is a "wake word" when you say "Alexa," it listens to your voice. Every intelligent gadget responds when you pronounce the wake word. Although Alexa is the voice assistant that is officially recognized, users can change it to a different wake word such as "Amazon," "Computer," as well as "Echo" as an alternative to waking up.

Alexa and Conversational AI

Today, many machines are deployed for speaking or having conversations with humans. It can be a support agent as an online bot or, here, Alexa. The difference between these technologies is known by how intelligent it is, and a better Conversational AI makes the difference through features like

1. Automatic speech recognition

2. Natural Language Processing/Natural language processing

3. Text-to-Speech (TTS) with voice synthesis

The audio waveform is transformed to text at the ASR step when anyone asks a query to the application. The device interprets the questions at the NLP stage and gives a smart response. The text transforms into audio signals during the TTP step, and then audio is created for the user. A variety of deep-learning models can be linked to a pipeline to create an interactive AI application. A familiar pipeline is also present in Alexa too.

1. Automatic speech recognition

Automatic speech recognition "Automatic Speech Recognition (ASR) technology converts words spoken into text. 1st step for enabling voice technologies like Amazon Alexa responding when asked, "Alexa , what's it like outside?". ASR, voice technology, detects spoken sound and recognizes it as words. ASR is the basis of the voice experience. It lets computers comprehend our most natural way of communication (speech).

Before, ASR was simply an audio recording in a computer's mind. With ASR, computers can detect patterns in audio waveforms, match them with the sound in a given language and eventually identify which words the user spoke. And ASR, as well as voice assistants, are getting smarter and smarter with the help of generative AI.

2. Natural Language Understanding

Natural language understanding allows computers to understand what the speaker is saying and not just what they speak. This allows the voice technology of Alexa to conclude that you're likely seeking an accurate local weather forecast when you ask, " Alexa, what's it like outside?" At times, voice-first technologies are constructed using NLU, which is focused on recognizing patterns and meanings in human spoken language. A computer is able to understand the meaning of what you are saying without the need to express it in a particular way. Using your voice, you will appear in a real conversation.

A microphone symbol showing speech recognition via sound waves.

Before NLU developing an app for weather that uses voice input required the creation of a list of different applications. The concept of the fact that "is it pouring rain" or "is it likely to rain" is the same thing. This kind of flexible voice experience allows it to provide a quicker, easy, more convenient, and more enjoyable way to interact with technology. Voice technologies are integrated into NLU, making it possible for developers to focus more on the design of effective voice experiences, and less on trying to figure out the meaning of what users are trying to convey.

Four methods for designing a natural voice-first experience: Understanding intent:

A. Identifying intent: Know which different goals someone wants to accomplish and be specific.

B. Identify utterance: What types of words or phrases would people use to signal their goal and intent?

C. Cover corrections: Natural conversion is not perfect. So allow the users to correct errors or change the answers.

D. Grow expectations: It is more ideal to say, " I do not know the answer to that question," than to pretend to be right by giving a wrong answer.

3. Text-To-Speech

TTS module enables the Alexa Auto SDK client application to synthesize Alexa speech on demand from text-to-speech synthesis Markup language (SSML) string. Synthesize speech, and this module uses a text-to-speech module. Auto SDK does not provide any speech-playing API. Your application TTS module integration is responsible for playing synthesized speech delivering a unified Alexa experience to the user.

Text-to-speech model does not require engine configuration.

Teaching a computer to interpret with voice:

Communication is a constant activity for deciphering meaning. Many times we even use the wrong words. Usually, the words we say are not the words that we mean. NLU is all about giving computers the necessary understanding of the context for what we say and the flexibility to comprehend different ways in which we can say the same thing.

Alexa learns from human data continuously -

The data is the crucial strength of Alexa. Each time Alexa is unable to interpret your request correctly, the data is enhanced by the system's wisdom to be used the next time. Understanding natural human speech is a huge challenge, but today we have processing capability for improving it as we keep using it more and more. Natural processing capabilities for language have risen to an entirely new level, even though the human language is very complicated.

Amazon has experts in making improvements to Alexa as well as Alexa voice services, as well as an array of machines. The objective is to make a spoken speech as natural as they can

How is Machine learning helping Alexa's growth?

Alexa gets smarter with time. As humans do, Alexa has learned to continue conversations from one inquiry to another. If you aren't sure of the exact name of the capability you would like Alexa to master, you might be summoned by getting close to it.

Furthermore, when you use Alexa hunches or smart home devices that are connected to the smart home Alexa hunches and smart home devices, Alexa will inform you when any pattern is not working, such as lighting being on for too long, or if the door is locked and then suggest fixing it for you.

These significant advancements were achieved in conversational and sophisticated voice assistants a few years ago, and machine learning is how these advances were achieved.

A microphone symbol demonstrating the capability of AI assistants in various kinds of roles such as music, shopping, alarm etc.

How does VUI make human-machine interaction conversational?

Conversations are emotionally and conceptually complex. When we as humans interact, how we say something makes a big difference, just what we say.

It is difficult for computers to grasp these nuances, which is where voice design gets in. Well-designed VUI is flexible and takes these unwritten rules of conversation.

Five elements to remember while designing a seamless virtual assistance technology:-

Variety

Conversations are much more than understanding words, sentences, and perspectives. Humans like to interact in natural and unconventional ways by using phrases they prefer. They correct or change the direction during a dialogue.

Context

Everyday conversations are taken into account more than the words that are spoken. Positive conversations include context like why, when, and where. I.e., "Alexa, set a reminder for car wash tomorrow" is a simple request during the daytime, but at 1:00 am, "tomorrow" means something different.

Engagement

Conversations can be inconsistent and dynamic. Both parties must understand, respond and remember what the participant is saying. The VUI is designed so that Alexa captures all the critical parts of a conversation, no matter how well or poorly presented. I.e., if a user says both the date and the destination to book a flight. Alexa would not have to ask any follow-up questions like "What is the departure date or which destination do you want to book a flight for?"

Tone

Face-to-face conversations are mostly done with emotion and personality and even have surprises. If the conversation with Alexa is appropriate, it will make you feel that you are having a conversation with a human and will not make you think that you are having a conversation with a machine.

Memory

Conversations do not happen out of isolated occurrences. When appropriate, voice-first experience accounts for the introductory text making the conversation more engaging and relevant to the owner. This is built and updated on previous questions and answers. This can be from five minutes ago to over five days ago.

There are many various layers to creating and developing a natural conversational experience. For example, you must present the most relevant information concisely and straightforwardly. It gradually builds users' responses with follow-up questions and information. But we must remember that we often use a combination of words and phrases that mean the same. Sometimes the user may ask irrelevant questions, and sometimes the user may crack jokes. A well-developed and designed voice experience can become a conversation and takes all these elements to account.

Conclusion

Artificial intelligence is not just advancing on its own but is also helping to develop other technology. AI was said to be harmful and would even discard many jobs. But in the real world, it paved the way for many advancements in technology, not just for revenue purposes but simplifying daily lives. NLP is one of the technologies accelerated by artificial intelligence.

Learn artificial intelligence, machine learning, and data science to create and develop much more devices like Alexa, Siri, or Cortana as voice recognition and other voice-based applications are needed for businesses for automation and even to improve user experience and interaction.


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