Building a Multilingual AI Assistant with Spitch

Introduction

Multilingual AI assistants like Google Assistant, Apple Siri, and Amazon Alexa have made rippling waves in the tech field, with use cases cutting across education, scheduling, research, virtual assistance, and so on. These assistants now support more languages, with GPT 4+ being the most multilingual, with language support for over 50 languages.

Multilingual AI assistants help businesses and developers create more inclusive and accessible digital experiences for their customers. They bridge the communication gap between users and companies. 

One major setback with these assistants is that they often prioritize high-resource languages over low-resource languages. This can reduce the accessibility and inclusivity for speakers of underrepresented languages, limiting their ability to benefit from AI-driven automation. But Spitch is now changing the game!

You can think of Spitch as the superhero fighting for the inclusion of low-resource African languages in applications and businesses. Spitch is breaking down barriers with cutting-edge speech-to-text, text-to-speech, machine translation, and diacritization technology. Now, businesses and developers can build solutions and products that include African languages and cater to diverse linguistic needs.

In this article, you will be introduced to multilingual AI assistants, and how to build one using Spitch.

What is a Multilingual AI Assistant?

Before you can know what a multilingual AI Assistant is, it is imperative to understand what an AI Assistant is first. According to TechTarget, an AI Assistant is a piece of software that uses artificial intelligence to understand natural language voice commands and complete tasks for the user.

Therefore, a multilingual AI assistant is an advanced AI-powered software designed to comprehend voice commands, execute user tasks, respond to queries, and/or provide seamless support across multiple languages.

Unlike traditional AI assistants that operate in a single language, multilingual assistants leverage Natural Language Processing (NLP), Automatic Speech Recognition (ASR), and multiple small language models to interpret, translate, and generate responses in different languages.

Multilingual AI assistants help businesses reach more people and cultures. Instead of developing separate software for each target region, organizations can streamline their reach with a single, powerful solution that can easily be adapted to multiple languages.

Types of AI Assistants

Before going ahead to build an AI assistant, it is important to define the scope of the bot. To define the scope of the bot, two of the most important factors to consider are the mode of interface and the purpose of the AI assistant.

Types of AI Assistants based on Mode of Interface

1. Text-Based AI Assistants: Text-based AI assistants interact with users through written text, typically via messaging apps, websites, or email. These assistants rely heavily on NLP to understand queries and generate responses. They are widely used in customer support, automated chatbots, and online help desks. Some popular examples of text-based AI assistants include OpenAI’s ChatGPT, Facebook Messenger bots, and Zendesk’s Answer Bot.

2. Voice-Based AI Assistants: Voice-based AI assistants are designed to process spoken language and respond using synthesized speech. They leverage ASR and text-to-speech (TTS) technologies to enable hands-free, conversational interactions. These assistants are often integrated into smart speakers, smart home devices and mobile devices. Some notable examples include Amazon Alexa, Apple’s Siri, and Google Assistant.

3. Hybrid AI Assistants: As the name implies, hybrid AI assistants combine text and voice interfaces, offering users the flexibility of switching between typing and speaking. They provide users with greater accessibility and can cater to users in different environments. Microsoft’s Cortana and Google Assistant support text and voice inputs, making them versatile tools for various tasks.

4. Graphical AI Assistants: These AI assistants incorporate visual elements such as avatars, interactive dashboards, or augmented reality (AR) features into their workflows. Graphical AI assistants are designed for a more engaging user experience and are often used in virtual customer service or gaming environments. Some examples include Samsung’s Bixby, which integrates visual search, and Replika, an AI-powered chatbot with an animated avatar.

Types of AI Assistants based on Purpose

1. Chatbots: Chatbots are AI-driven programs designed to simulate human-like conversations for customer support, e-commerce, research, and general inquiries. Chatbots typically handled predefined questions but with the advent of generative AI, chatbots are now able to respond to questions using prompt engineering. Examples include Drift for sales automation, Intercom for customer engagement, and Spitch-powered AI bots.

2. Personal Assistants: Personal AI assistants help individuals with daily tasks, such as setting reminders, managing schedules, and retrieving information. They often integrate with multiple apps and services to enhance the user's productivity. Popular examples include Apple’s Siri, Amazon Alexa, and Google Assistant, which assist users with smart home control, calendar management, and weather updates.

3. Conversational Agents: Conversational AI agents are designed for interactions that are context-aware, and often used in social AI and advanced customer service applications. These agents utilize machine learning and NLP to provide meaningful, human-like conversations. Examples include OpenAI’s ChatGPT, IBM Watson Assistant for enterprise solutions, and Mitsuku, an award-winning conversational chatbot.

4. Specialized Virtual Assistants: These AI assistants are tailored for specific industries, such as healthcare, finance, academic, or legal services. Like expert systems, they provide professional guidance, automate workflows, and assist professionals with domain-specific tasks. Notable examples include IBM Watson Health for medical insights, Amelia by IPsoft for enterprise automation, and Spitch’s AI solutions for speech recognition in African languages.

Why do we need multilingual AI Assistants?

It’s no news that the world is becoming increasingly connected, and businesses, governments, and even individuals like you and I, need technology to bridge language gaps. Multilingual AI assistants enable seamless communication across diverse populations, improving customer service, accessibility, and user engagement. Whether in global commerce, healthcare, politics, or education, these assistants help break down linguistic barriers, allowing users to interact with technology in their preferred language. 

However, while major languages like English, Spanish, and Mandarin dominate AI development, low-resource languages are often ignored. The lack of large datasets and commercial incentives means that many African and indigenous languages are not supported by mainstream AI assistants. You can imagine what this means for the average African who doesn’t speak one of these mainstream languages.

Spitch is addressing this challenge by developing cutting-edge language technologies for African languages. As the leading speech and language AI company focused on Africa, Spitch offers speech-to-text, text-to-speech, machine translation, and tone-marking tools that empower businesses to build multilingual solutions.

Workflow for building a multilingual AI assistant with Spitch

To build a multilingual AI assistant, you need to plan carefully, choose the right tools, and have the right resources for implementation. With Spitch’s advanced speech and language processing technologies, developers can create AI assistants that support multiple languages, including low-resource African languages. If you want to build a multilingual AI assistant with Spitch, here is a structured workflow to help you.

Multilingual AI Assistant Workflow with Spitch

Step 1: Sign up on Spitch and get access to your API keys

The first step is to create an account on Spitch and obtain your API keys. You’ll need this to integrate Spitch into your AI assistant for language translation. Be sure to store your API keys securely.

To get access to your API keys on Spitch, log into the developer portal and click on the API Keys section highlighted below. 

Spitch Developer Portal

Step 2: Requirements Gathering

You need to specify the software requirements of the assistant in a Software Requirements Document (SRD) before diving into development. In this phase, you need to clearly define the purpose and scope of your AI assistant, as well as the key languages you want to support, the interface, and other technical specifications like deployment options, programming languages, and so on.

It would help to do market research at this stage to analyze the specific needs of your target audience. Also consider factors such as voice input, text-based responses, domain-specific terminology, and the level of conversational complexity required. Finally, choose the appropriate Spitch features for your AI assistant, such as speech generation and transcription for voice interactions and machine translation for multilingual text processing.

Step 3: Train the AI model

A well-trained AI assistant needs a strong foundation of knowledge. Depending on your use case, this can be an already pre-trained reasoning LLM (like GPT-4.5 or Claude) or a custom-trained model fine-tuned on your FAQ database, common user queries, response formats, and internal business details. If your AI assistant is a chatbot, fine-tuning will help it generate more accurate responses tailored to your needs.

Once the AI model generates text-based responses, Spitch’s API can enhance multilingual capabilities by translating the text output into the desired language before converting it into speech for voice-based interactions.

Step 4: Implement Multilingual Support using Spitch Machine Translation feature

To ensure seamless multilingual interactions, integrate Spitch’s machine translation feature. This will allow your AI assistant to dynamically translate user queries and responses across different languages. By using Spitch’s translation API, the assistant can detect a user’s preferred language and deliver responses in real time, ensuring a natural and inclusive conversational experience. More information on the Spitch translation feature is available here.

To implement multilingual support in your AI assistant, you need to ensure that each query the user sends and each response the user receives is being translated into a baseline language (probably English but should depend on your assistant’s knowledge base). To do this, make use of the code below.

from spitch import Spitch
import os

os.environ["SPITCH_API_KEY"] = “YOUR_SPITCH_API_KEY”
client = Spitch()

translation = client.text.translate(
    text=user_input,
    source=in_lang,
    target=target_lang,
)
return translation.text

PS. If you’re also looking to enable a voice feature on your assistant that converts speech to text and text to speech, Spitch also has speech generation and speech transcription features that you can check out in our docs. You can use this code below if you’re looking to implement speech-processing features as well.

# Import modules
from spitch import Spitch
import os

# Instantiate a Spitch client
os.environ["SPITCH_API_KEY"] = os.getenv("Spitch")
client = Spitch()

def text_to_speech(input_text, input_language, input_voice, audio_path='generated_audio.wav'):
    with open(audio_path, "wb") as f:
        response = client.speech.generate(
            text=input_text,
            language=input_language,
            voice=input_voice
        )
        f.write(response.read())
    return audio_path

def speech_to_text(input_audio, input_language):
    with open(input_audio, "rb") as f:
        response = client.speech.transcribe(
        language=input_language,
        content=f.read()
        )
    return response.text

Step 5: Create an API and deploy!

Once your assistant is fully trained and multilingual support is implemented, it's time to deploy. Create an API (Application Programming Interface) endpoint that connects your chatbot with Spitch’s services and integrates it into your desired platform, whether a website, mobile app, or messaging service. 

When it comes to deployment, it is important to choose the right technology stack. At this point, feel free to refer to your SRD, but be open to change if the selected deployment option no longer meets your requirements. Remember that all the work you’ve done in gathering your requirements and training your model all boils down to how it is presented to your customer. The user interface, API endpoint, and deployment service have to make the overall customer experience worthwhile for your customer.

Concluding remarks

Building multilingual AI assistants is no longer a complex project that can only be completed by big tech companies. Developers and entrepreneurs can build affordable multilingual AI assistants using pre-trained models and Spitch. Spitch’s state-of-the-art technology can perform speech transcription, speech generation, language translation and diacritics restoration.

This article provided a step-by-step guide to building a multilingual AI assistant and demonstrated how to implement multilingual support for African languages such as English, Yoruba, Hausa, and Igbo using Spitch. AI assistants are game changers for applications and businesses but multilingual AI assistants help you stand out. 

Now is the time to build AI assistants that speak your users’ language—start with Spitch today!