How Much Does it Cost to Build Voice Translator App?

8 min readFeb 8, 2023

Voice translator apps have become increasingly popular in recent years, enabling communication in different languages without any language barrier. Whether you’re traveling abroad, conducting business with international clients, or simply trying to communicate with people who speak another language, voice translator apps can be valuable. However, one of the major questions businesses have when considering developing or investing in a new app is, “How much does it cost to build a voice translator app?” The cost of developing a voice translator app can vary greatly depending on many factors, such as the app’s complexity, the number of languages and features it supports, the dev team’s location, and its experience. In this article, we will explore the different factors that can affect the cost of building a voice translator app and provide an estimate of what you can expect to pay to develop one.

Understanding The Market Insights of the Global Voice Translation Sector

With the surge of new technologies like 5G, artificial intelligence, and machine learning, people expect the digital world to be smart and helpful. That’s why the role of voice translators is extremely huge. Furthermore, we expect a globalized economy where language barriers are the last thing people want to experience. To reinforce the progress and support global processes of service delivery, more and more businesses are investing in translation apps, and voice translators are the next step to evolve.

  • The translation services market, valued at $40.2 billion in 2021, is projected to grow to $53.5 billion by 2032. The global speech-to-speech translation market, valued at $335 million in 2020, will nearly reach $576 million by 2026.
  • One of the key drivers of the global voice translator sector will be the growing demand for business process outsourcing (BPO).
  • North America will account for 45% of the market, with Asia Pacific being the fastest-growing region.
  • Voice translation adoption is focused on the B2C market. The B2B market is actively working on integrating technology into everyday business processes.

Why is the translation app important?

The translation that was once available only through physical dictionaries played an absolutely different role in business processes and people’s lives. We used to carry small phrasebooks when traveling abroad. Yes, it was romantic, but nothing more. Today, users have everything on their smartphones, and voice translators were just in time to make translation processes even more straightforward.

Communication: Translator apps play a crucial role in facilitating communication across language barriers, enabling individuals who speak different languages to easily converse with each other, thus breaking down barriers and promoting greater understanding.

Reach new markets: Speech-to-speech, text-to-text, and text-to-speech translations facilitate new markets’ outreach without communication delays and expenses on human translators.

Business: In the business realm, these apps allow companies to expand their customer base by providing the means to communicate with potential clients in their native languages.

Travel: For travelers visiting foreign countries, translator apps are a valuable resource for understanding signs, menus, and other written materials.

Education: Similar apps assist students in learning a new language by providing translations of unfamiliar words and phrases.

Access to information: Translation apps can provide access to information that would otherwise be inaccessible due to language barriers.

Cultural understanding: Translator apps play a key role in fostering cultural understanding by providing translations of cultural texts such as literature and news articles.

How do language translation apps work?

Language translation apps typically use machine learning algorithms, specifically neural machine translation (NMT), to translate text from one language to another. NMT is a type of deep learning algorithm trained on large datasets of bilingual text, which allows it to learn the patterns and relationships between words and phrases in different languages.

The basic architecture of an NMT system consists of an encoder and a decoder. The encoder takes in a sentence in the source language and converts it into a fixed-length vector representation called the context vector. This vector captures the meaning of the input sentence and passes it to the decoder, which in turn generates the corresponding translation in the chosen language.

A large corpus of bilingual text is used to train the NMT model. The model is trained to maximize the likelihood of a correct translation given the source sentence. During the training process, the model learns the underlying relationships and patterns between the source and target languages, which allows it to generate more accurate and natural-sounding translations.

Once the model is trained, it can be used to translate new sentences that it has never seen before. When a user inputs text into the app, the NMT model uses the learned relationships and patterns to generate a translation in the desired language. The app can be fine-tuned to improve the translation by using additional data, such as user feedback, to make the translation more accurate.

Additionally, some apps also use pre-trained models with a lot of data to speed up the process and improve the accuracy of the translations.

In voice translator applications, the technology and processes differ. Voice translator apps use a combination of several different technologies to function, including:

Speech Recognition: This technology is used to convert spoken language into text. It relies on algorithms that analyze the sound waves of speech and match them to the corresponding words or phrases.

Machine Translation: This technology is used to translate the text generated by the speech recognition technology into the desired language. It uses complex algorithms and machine learning models to analyze the grammar and structure of the text and then generate a translated version.

Text-to-Speech: This technology is used to convert the translated text back into spoken language. It uses computer-generated speech to read the translated text aloud.

Natural Language Processing: This technology is used to analyze and understand the meaning of the text. It helps the app identify the text’s intent and provide a more accurate translation.

Language identification: This technology helps identify the language of the source text. It allows apps to automatically detect the language spoken and translate it accordingly.

Cloud-based Services: Some apps use cloud-based services to access machine learning models and powerful servers to process the translations in real-time.

Offline functionality: Some apps can store and access previous translations for offline use or save frequently used phrases for easy access.

What are the features of a translator app?

A translator app can have multiple features, but some common ones include

  • Language support: Support multiple languages, so users can translate between different languages.
  • Speech recognition: Recognize and transcribe spoken language in real-time, so users can speak into the app and have it translated.
  • Text-to-speech: The ability to read translations aloud, so users can listen to the translated text.
  • Offline support: The ability to access translations even without an internet connection.
  • Camera translation: Use the camera of their device to translate text in real-time, such as signs, menus, or documents.
  • Handwritten translation: Write text or draw characters by hand and then translate it.
  • Dictionary functionality: Built-in dictionaries to look up words and phrases in the target language.
  • Phrasebook functionality: Allow users to save frequently used phrases for easy access.
  • Language identification: Automatically detect the source text language and translate it accordingly.
  • Multi-person conversation: The function to support multi-person conversation in different languages with simultaneous translation.

Tech Stack and App Development Team

The technical stack required to build a voice translator app would depend on the specific requirements and features of the app, but in general, it would likely involve the following:

Speech recognition: Technology such as Google Speech-to-Text, Amazon Transcribe, or Microsoft Azure Speech Services can be used to convert spoken words into text. These services use deep learning algorithms to process the audio input, generate a transcript, and adapt to different accents and noise levels.

Machine learning: A neural machine translation (NMT) model would be used to translate the text from one language to another. Tensorflow (by Google), PyTorch (by Facebook), or other frameworks can be used to train and implement the NMT model.

Natural Language Processing (NLP): Techniques such as tokenization and stemming would be used to process the text input and improve the translation quality.

Mobile development: Implementing a mobile development platform such as React Native, Flutter, Xamarin, or native development for iOS and Android devices.

Backend development: Backend technology such as Node.js, Ruby on Rails, or Python could be used to handle the app’s server-side logic, user authentication, data storage, and integration with other services.

Database: A database technology such as MongoDB, Firebase, MySQL, or PostgreSQL could be used to store and retrieve user data and translations.

Our development team for the voice translation app will include the following:

Project manager: To lead the development process, ensure that the project stays on schedule and within budget, and act as a dedicated relationship owner supporting communication.

Mobile developers: The specialists will work on the user interface and the UX, building the app’s front end and handling the integration with the NMT model and speech recognition technology.

Backend developers: Developers will work on handling the app’s server-side logic and integration with the database, making sure the functionality is working as expected and is scalable.

Machine learning engineers: The team will work on the app’s core functionality, train, and implement the NMT model, ensuring the translations are accurate and natural-sounding.

Quality assurance (QA) testers: Software engineers at the test will work on the app’s quality, ensuring its reliability and functionality.

Data scientists: The team will work on the app’s preprocessing and postprocessing, ensuring the translations are as accurate as possible.

How Much Does It Cost to Create a Voice Translator App?

In general, a basic voice translator app development cost would be anywhere from $25,000 to $30,000, with a limited number of languages and basic features. An app with more advanced features and support for multiple languages could cost upwards of $50,000 to $100,000 or more.

The cost of creating a voice translator app can vary greatly depending on many factors, including the app’s complexity, the number of languages and features it supports, and the dev team’s experience.

It’s also worth noting that development costs aren’t the only thing to consider. There are additional costs for maintaining the app, including hosting, licensing, and updates. Additionally, you’ll have to consider marketing and promotion costs to monetize the app.


It has been quite a journey to reach this point, and we thank you for your commitment! Our initial objective with this article was to demonstrate the intricate architecture of a voice translator application to an audience outside of the tech world. The next focal point of the article was to reveal the immense potential of voice translation apps for investors and entrepreneurs who are in search of new business opportunities.

Now, with an answer to the question “How much does it cost to build a voice translator app?” it is clear where to start and how much you will need as an initial investment for a voice translator app.




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