Can I create my own voice assistant?

The use of voice assistants like Amazon Alexa, Apple’s Siri, and Google Assistant has risen substantially in recent years. According to the Pew Research Center, the number of adults in the United States who use voice assistants tripled between 2017 and 2021, from 7% to 21% ( This growth reflects the convenience and capabilities voice assistants provide.

Creating your own custom voice assistant allows you to take full advantage of this technology and tailor it exactly to your needs. With a custom voice assistant, you can streamline tasks, automate workflows, control devices, access information, and more through simple voice commands. You get all the benefits of existing voice assistants while customizing the experience.

In this guide, we will cover everything you need to know to build your own voice assistant. We will look at the necessary hardware and software components, including speech recognition, natural language processing, and speech synthesis. We will also discuss how to create an interaction model, choose a hosting platform, and test your assistant. By the end, you will have the knowledge to create a personalized voice assistant that simplifies your life.

Voice Assistant Capabilities

Voice assistants are software programs that can understand natural language voice commands and complete tasks for users. Some core capabilities of modern voice assistants include:

Voice recognition – Voice assistants use speech recognition algorithms to listen to and transcribe spoken commands. They can continuously listen for a “wake word” then process voice input. Popular voice assistants like Siri, Alexa and Google Assistant can recognize a wide vocabulary of words and phrases.

Natural language processing – Beyond just transcribing speech, voice assistants can actually understand the meaning behind commands using natural language processing. This allows them to interpret more complex requests.

Speech synthesis – Voice assistants can respond to voice commands by generating computerized speech. Text-to-speech technology converts their responses into natural sounding verbal answers.

In terms of capabilities, voice assistants can control smart home devices, play audio, look up general information, set reminders and more. Advanced assistants are gaining contextual awareness and personalization. They can handle multi-turn conversations to clarify commands and access previous context when needed. Overall, voice assistants aim to provide an intuitive hands-free interface to technology through conversational speech.

Necessary Hardware

To build your own voice assistant, you’ll need hardware for speech input and output, as well as a device to run the software. Here are the key components:

For capturing speech input, you’ll need a microphone. USB microphones like the Blue Yeti or Samson Meteor provide high quality audio capture. You could also use a microphone module like the ReSpeaker that connects directly to a Raspberry Pi.

For speech output, you’ll need speakers or preferably a speaker with audio output like an Amazon Echo Dot. This allows the voice assistant to respond to your commands and queries verbally.

For processing, you will need a device like a Raspberry Pi or a desktop PC. The Raspberry Pi is inexpensive but may lack some processing power. A more powerful multi-core PC will handle voice recognition and synthesis smoothly.[1]

Having the right microphone, speaker, and computing device will provide the necessary hardware for creating your own voice assistant.

Speech Recognition Software

To enable speech recognition capabilities, you’ll need to implement speech recognition software. There are a few different options:

  • Open source software like Kaldi, CMUSphinx, and Julius. These provide the underlying speech recognition models and engines to transcribe audio into text locally on your device.
  • Cloud speech APIs like Google Cloud Speech and Amazon Transcribe. These provide speech recognition as a service by sending audio to their servers for processing.

The open source options give you more customization and control, since you host the models yourself. However, the cloud APIs are easier to implement and can provide higher accuracy. The cloud options have associated costs based on usage, while open source solutions are free.

For a DIY voice assistant, open source software like Kaldi or CMUSphinx may provide the best balance of accuracy and control. They allow you to iteratively improve the speech recognition models over time. Cloud APIs are better suited for quick implementation or proofs of concept.

Natural Language Processing

Natural language processing (NLP) refers to the ability of a computer program to understand and analyze human language. This is a critical component of voice assistants, allowing them to interpret the meaning behind spoken commands and questions.

Some popular open source NLP libraries used for building voice assistants include Rasa NLU, Snips NLU, and the Natural Language Toolkit (NLTK). These provide capabilities like intent classification, entity extraction, and sentiment analysis out of the box.

Cloud platforms like Dialogflow and LUIS also offer NLP as a service, allowing developers to easily integrate language understanding into their voice apps.

At a high level, NLP is about transforming unstructured text into structured, meaningful data. This involves techniques like tokenization, lemmatization, part-of-speech tagging, named entity recognition, and semantic analysis to extract intents, entities, and context from natural language.

Speech Synthesis

Speech synthesis refers to the artificial production of human speech. To enable speech capabilities, we need a text-to-speech (TTS) engine that can convert text into natural sounding audio.

There are several open source TTS engines available, such as eSpeak and MaryTTS. eSpeak uses formant synthesis to generate speech and supports many languages, while MaryTTS utilizes deep neural networks for more natural sounding voices.

In addition to open source options, there are also cloud-based speech APIs like Amazon Polly and Google WaveNet that provide easy access to high quality voices. The cloud services typically sound more natural and human-like compared to most open source engines.

When evaluating TTS engines, factors like naturalness, pronunciation accuracy and diversity of voices should be considered. While open source options are free, cloud APIs provide superior audio quality and a wider selection of natural voices.

Interaction Model

The interaction model defines the various ways users can interact with the voice assistant and how the assistant will respond. This includes defining sample utterances, conversational flows for key use cases, and handling off-topic interactions.

Some common utterances to support might include:

  • “What’s the weather today?”
  • “Set a reminder for tomorrow at 9am”
  • “Play some music”

The interaction model should map out conversational flows for core capabilities like checking the weather, news, calendar, and reminders. At each step, the assistant should provide clear prompts and have fallback intents if the user goes off the expected path.

It’s also important to account for off-topic questions, small talk, and jokes. The assistant can use fallback responses like “I don’t have an answer for that” or humor like “I’m afraid I don’t have much of a sense of humor yet.” Having variety in these fallback utterances makes interactions feel more natural.

Well-designed interaction models anticipate likely use cases and guide users through an intuitive conversation. Extensive testing and iteration is key to improving the model over time based on real-world usage.[1]

Hosting Platform

There are a few options for hosting a custom voice assistant. You can self-host the assistant directly on a device like a Raspberry Pi, which gives you full control but limited scalability. Or you can leverage cloud hosting platforms like AWS Lambda or Google Cloud Functions to handle on-demand scaling and availability.

Self-hosting on a device you manage gives you maximum privacy and customization ability, since you control the full software stack. Projects like Mycroft are designed to be installed on Linux or Raspberry Pi. However, the device will be limited in how many concurrent users it can handle before performance degrades. And if the device goes offline, so does your assistant.

For higher scalability and reliability, cloud functions offer convenient pay-as-you-go pricing models. They can scale to support many thousands of users out of the box. The downside is less control over the stack, dependence on the cloud provider’s services, and ongoing hosting costs. Performance and costs will need to be monitored as traffic grows.

Testing and Iteration

Once you have an initial version of your voice assistant working, it’s crucial to test it out with real users and gather feedback to make improvements. Start by trying out your assistant with a small group of friendly testers who can provide honest feedback on the experience. Pay attention to the accuracy of the speech recognition and natural language understanding. Note where the assistant fails to understand requests or provides irrelevant responses so these can be improved.

Gather subjective feedback from testers on the usefulness and engagement of the voice assistant. Note any friction points or lack of interest. Iterate based on this feedback to expand the assistant’s capabilities and improve the conversational experience. As capabilities expand, continue testing with more users and use cases. Treat the launch as a minimal viable product, and plan on constant improvement and expansion over time.

Tools like automated testing and try unexpected inputs can complement real user testing. The goal is to continuously refine the assistant until it delivers a seamless, useful voice experience.


In this guide, we covered the main components needed to build your own voice assistant. First, we looked at the necessary hardware like a microphone and speaker. Then, we explored the speech recognition software required to convert speech to text. Next, we discussed how natural language processing enables the assistant to understand requests. We also covered text-to-speech software for the voice response. Additionally, we examined creating an interaction model to define how the assistant handles requests. Finally, we recommended different platforms for hosting the assistant.

To take your skills further, check out resources like the Mycroft AI community ( and Mozilla Common Voice project ( for datasets and open source tools. Consider joining forums and online groups to connect with other voice tech builders and designers.

We hope this guide provided a solid foundation for creating your own customizable voice assistant. With some dedication and iteration, you’ll be well on your way to building a unique voice experience. The possibilities are endless, so start tinkering and have fun bringing your assistant to life!

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