For the purpose of building in voice and natural language capabilities into your own applications, one has to depend on cloud options. But if you are wondering why limit yourself only to Amazon or Apple, and create your own, you are on the right track. Anyone can build their system and enable the voice with multiple devices. It is only the difference of speech to text, followed by a query parser, a pipeline, a rules engine and of course a pluggable architecture with mandatory open APIs.
All of these artificial intelligence friends have been living in our houses, cars and phones. Voice assistants have changed the way we live, we no longer use our fingers to generate a search, we get it done only by instruction. So the multimillion dollar question is, how to create an app like Alexa?
Why are apps like Siri and Alexa so famous?
- Simple to use: the interface is brilliantly simple to use, relieving the user completely. Most of the work is done by the app.
- Fast: we have all experienced the fact that voice is quicker than touch recognition. All one needs to do is ask! How simpler can it get? Communication is something that comes naturally to people, and these apps make use of this feature.
- Emotional attachment: it has been seen that the users build up a deep sense of emotional attachment to the voice assistants. These apps create an illusion of attachment with the users which adds on to the intimacy factor. It becomes an essential point of contact between the brand and the customer.
- Fascinating: children are enthralled by the functions these apps can perform, hence they will in all probability soon not be able to imagine a world without it.
Technologies in Mobile Assistants:
Speech to text (STT) engine: this engine converts the user’s voice to text, the voice could necessarily be the user’s voice or any random audio clip.
Text to speech (TTS) engine: this converts the text to speech, and is exceptionally useful when the user is engaged in some other activity. This plays a huge role in humanizing the assistant.
Tagging (Intelligence): this helps the voice assistant to understand what the user is trying to say.
Noise reduction engine: this is to cancel out the external environment noise, else it will be too many stimulus for the app to process.
Voice biometrics: this is a process of authentication, so that the app understands that it is specifically your voice.
Voice recognition: this drives all personal assistant apps. This technology puts sense into the app such that it can decipher your language.
User interface: this consists of two parts, the voice and the call out, voice part is where the user hears the result to the question he asked, and call outs are where he sees the results on the screen.
Speech compression engine: this compresses the users voice so that it reaches the server even faster.
How to make a voice assistant like Alexa
There are three methods how you can work this out – Junior method wherein you can integrate voice assistant technology to your mobile app with the help of APIs. You could also do the middle method, wherein you create a voice assistant by using open source services and APIs. And the most senior method is to develop a voice assistant from scratch and then integrate it into the mobile app.
This method is based on the integration of trusted technology into existing apps. For the same, one needs to get the kit, and integrate it into the mobile app. The kit defines the intents as types of requests, and then to clarify the types, one simply needs to group it into domains. This is how basically the junior method works.
This method is recommended for those who are familiar with machine learning. There are some tools which can be used for making an AI assistant app, along with mobile and web services:
- Melissa: it is capable of speaking, taking notes, reading the news, so this is a perfect fit for those who are new to creating voice assistant apps.
- Jasper: for those who want to create most of the chunk of the app, this is the best choice. It runs on Raspberry Pi’s Model B, and it is capable of listening and learning.
This is for all the hard core developers, who already have previous experience in developing machine learning apps right from the scratch.
- Google’s Tensorflow : this is an open source software library that has a flexible structure, and one can use this on various servers, mobile devices etc. one can use this easily as it is flexible and portable.
- Amazon Machine Learning: this is a machine learning service that helps the developers create a very complicated and intelligent machine learning app. This has the support of many data sources, and it lets the developers to create the data source objects using a MYSQL database in Amazon Redshift. This was made simple, scalable and dynamic flexible AML technology, making it one of the most used apps.
The idea of incorporating artificial intelligence is one of the top trends. Everyone wants to incorporate this app into their devices. Making a copy might be easy, but creating something from scratch is a difficult event. But if one follows the steps outlined above, it is possible to design an app just like Alexa.