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7 Proven Steps to develop Successful Multilingual Voice Assistants

Introduction about Voice Assistants

Voice assistants, at the crossroads of advanced artificial intelligence (AI) and natural language processing (NLP), have rapidly become an integral part of our digital experience. According to insider intelligence, as of 2023, an estimated 123.5 million people in the U.S. alone will use voice assistants at least monthly.

These intelligent personal assistants interpret human speech, perform tasks, and provide informative responses to a variety of inquiries. They have seamlessly integrated into our homes, vehicles, and workplaces, enabling us to set alarms, send texts, answer trivia, and navigate using GPS. This hands-free, eyes-free technology has fostered efficiency and convenience and played a vital role in assisting the disabled and the elderly with daily tasks.

Furthermore, these AI-powered tools are not just learning to understand what we say but also how we say it. By interpreting context and tone, they can now deliver a genuinely conversational experience. 

However, the true power of these voice assistants lies in their potential to become multilingual. As they gain the capability to understand and respond in multiple languages, they will unlock a new level of accessibility and global connectivity, establishing the multilingual voice assistant as a technological game-changer.

Example of Voice Assistants Available in the Market 

Voice assistants have grown in popularity and capability over the years, with several key players in the market demonstrating the power and potential of this technology.


  1. Amazon’s Alexa: Alexa is a cloud-based voice assistant that powers Amazon’s Echo and Dot devices. Alexa can answer questions, set alarms, play music, control smart home devices, and even tell jokes. Users can customize Alexa’s capabilities by enabling specific skills in the Alexa app. Alexa learns your speech patterns and personal preferences to improve its responses over time.
  2. Apple’s Siri: Debuting on the iPhone 4S in 2011, Siri was one of the first voice assistants to hit the mainstream market. Siri can handle various tasks, such as setting reminders, sending texts, making phone calls, and providing directions. Additionally, with the introduction of Shortcuts in iOS 12, Siri has become even more powerful, allowing users to create custom voice commands to execute a series of actions.
  3. Google Assistant: Available on Android devices, Google Home, and various third-party devices, Google Assistant leverages Google’s formidable search capabilities to answer questions, play music, control smart home devices, and manage tasks. It also supports a conversational mode, which enables a more natural dialogue between the user and the assistant.
  4. Microsoft’s Cortana: Initially integrated into Windows 10, Cortana can answer questions, set reminders, send emails, and manage calendars. Though it has taken a step back from consumer use, Cortana remains an integral part of Microsoft’s enterprise offerings, specifically within the Microsoft 365 suite.

All of these voice assistants come with unique strengths, but a shared goal amongst them is the aspiration to break down language barriers. Developing a multilingual voice assistant that can comprehend and converse in various languages would mark a significant step forward in this realm. 

As these systems evolve and learn from extensive datasets, the prospect of a genuinely multilingual voice assistant becomes ever more plausible and exciting.

Key steps to successfully developing a multilingual voice assistant

Developing a multilingual voice assistant is an ambitious and complex task, requiring a comprehensive understanding of numerous languages and cultures and an intricate knowledge of AI and machine learning techniques. Here are the key steps in the process:

  1. Gathering and Preparing Datasets

The first step involves gathering vast and diverse datasets representing multiple languages. These datasets are often collected from various sources, such as social media, customer feedback, and online text repositories. They provide various language styles, accents, and dialects. 

Preparing this data for use typically involves cleaning, formatting, and segmenting it into training and testing sets.

  1. Training the Model

The next step is to feed these datasets into a machine-learning model. This model uses natural language processing to analyze the data and generate patterns of speech and text in different languages. 

Training the model with data representing a range of sentiments, from positive to negative to neutral, helps in understanding the nuances of each language. It is much like how sentiment analysis services function.

  1. Testing and Refining

It needs to be tested after training the model. This involves using a portion of the data (not used in training) to check the model’s performance. Based on the results, the model can be further refined and trained using techniques like deep learning to improve its accuracy.

  1. Integration with Existing Systems

Once the multilingual model is developed and tested, it needs to be integrated into the existing voice assistant systems. This would involve interfacing with APIs, working with different software architectures, and ensuring the new multilingual capabilities do not interfere with existing functionality.

  1. Real-World Testing and Feedback Analysis

Real-world testing is essential after integration. The assistant should be tested by users who are native speakers of the languages it now supports. Feedback from these users is invaluable in further refining the model. 

Just like in the process of sentiment analysis, social media analysis, customer feedback analysis, brand reputation analysis, and opinion mining play significant roles in evaluating the system’s performance and identifying areas for improvement.

  1. Continuous Improvement

The work doesn’t stop after the launch. AI and machine learning systems learn and improve over time. Constant analysis of customer feedback, expectations, and product feedback will help the model evolve, and the assistant’s understanding of multiple languages will deepen. 

The systems should also be updated regularly to include emerging language trends and slang, a practice analogous to updating sentiment analysis in call centers.

The road to developing a multilingual voice assistant is complex, but the potential benefits are vast. A successful multilingual voice assistant breaks down language barriers and makes technology more accessible to a larger global audience. 

As we continue to refine AI and machine learning technologies, the dream of a genuinely multilingual voice assistant inches closer to reality.


Voice assistants are no longer a novelty; they are quickly becoming vital to our daily digital interactions. The rise of the multilingual voice assistant promises to be a significant leap forward, breaking down language barriers and fostering greater global connectivity.

The development of a truly multilingual voice assistant is a grand challenge, requiring vast datasets, sophisticated AI, machine learning models, and a deep understanding of multiple languages’ linguistic and cultural nuances. The path is complex, but the technology giants are making strides, bringing us closer to this reality.

In the end, the success of a multilingual voice assistant isn’t just about speaking multiple languages. It’s about understanding and respecting cultural differences, meeting customer expectations. These voice assistants have to deliver on the promise of AI: making life easier, more connected, and more universally accessible. This is the future we’re building, one word at a time.

Author Profile: Hardik Parikh


With more than 15 years of experience creating and selling innovative tech products, Hardik is an accomplished expert in the field. His current focus is building and scaling Shaip’s AI data platform, which leverages human-in-the-loop solutions to provide top-quality training datasets for AI models.

LinkedIn: https://www.linkedin.com/in/hardikvparikh/

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