Have you ever spoken to a voice assistant in Hindi and suddenly used an English word without even thinking about it?
For example, you might say, “Mera order dispatch hua kya?” or “Mujhe appointment reschedule karna hai.” This way of speaking is common in everyday conversations, especially in India. We naturally mix Hindi and English when the words come more easily.
For a person, understanding such a sentence is usually simple. But for a voice AI system, it can be more challenging. The system needs to recognize words from different languages, understand what the caller means, and respond correctly.
This is known as code-mixing. Hinglish, where Hindi and English are used together, is one of the most familiar examples in India.
In this guide, we’ll explain what code-mixing means in voice AI, how it differs from code-switching, why it can be difficult for voice systems, and what businesses should check before choosing a multilingual voice AI solution.
What Does Code-Mixing Mean?
Code-mixing refers to using words or phrases from two or more languages within the same sentence or spoken conversation. It is a natural part of communication when people use words from different languages to express themselves more comfortably or clearly.
For example:
- “Mujhe kal ka appointment reschedule karna hai.”
- “Please mera order kal deliver kar dena.”
In both examples, Hindi and English are used together in the same sentence. This type of language mixing is common in everyday conversations, particularly in multilingual countries such as India.
Hinglish is a common example of code-mixed speech, where Hindi and English are naturally combined during a conversation. For voice AI, this means the system may need to recognize and understand words from both languages within the same spoken request.
Code-mixing is related to how languages are combined during communication, regardless of whether the speaker later writes the words in Roman script, Devanagari, or another writing system.
What Is the Difference Between Code-Mixing and Code-Switching?
Code-mixing and code-switching are closely related terms, but they can describe different ways of using multiple languages in a conversation.
Code-mixing generally means using words or phrases from different languages within the same sentence. For example:
“Mera order kab tak deliver hoga?”
The sentence mainly follows a Hindi structure but includes English words such as “order” and “deliver.”
Code-switching generally refers to changing from one language to another during a conversation, often between sentences or speaking turns. For example:
“Mera order abhi tak nahi aaya. Can you check the delivery status?”
Here, the speaker starts in Hindi and then switches to English.
The distinction is not always consistent across linguistics and speech technology research, and the two terms may sometimes overlap. For voice AI, the key consideration is whether the system can understand natural language changes, whether they happen within a sentence or between different parts of a conversation.
Why Can Code-Mixed Speech Be Difficult for Voice AI?
Code-mixed speech can be easy for people to understand because we regularly switch between languages in everyday conversations. For a voice AI system, however, understanding the same speech can involve several challenges. The system needs to recognize the spoken words, understand the caller’s meaning, and respond appropriately.
1. Speech Recognition
Automatic speech recognition (ASR) converts spoken audio into text. With code-mixed speech, the system may need to recognize Hindi and English words within the same sentence, sometimes spoken with a regional accent.
Fast speech, background noise, unclear audio, or poor phone-line quality can make this process more difficult. A system may perform well when languages are spoken separately but make more mistakes when they are combined in the same sentence.
2. Language and Context
Recognizing individual words is not enough. The voice AI also needs to understand how those words are being used together and what the caller is trying to communicate.
For example, in “Mera card block ho gaya, but app mein active dikh raha hai,” the caller is describing a mismatch between the card status and what the app shows. The system needs to understand the complete context rather than focus only on the word “block.”
3. Intent Recognition
Once the speech has been converted into text, the system needs to identify the caller’s intent. A mixed-language sentence may contain important information that determines what action the system should take.
If the system misunderstands part of the sentence, it may identify the wrong intent, provide an irrelevant response, or fail to complete the requested task.
4. Response Generation
After understanding the caller’s request, the voice AI needs to generate an appropriate response. In a code-mixed conversation, the response may also contain words from more than one language.
The system should be able to deliver the response naturally and clearly. Incorrect pronunciation, unnatural language switching, or an unclear response can make the conversation difficult for the caller to follow.
5. Real-World Call Conditions
Voice AI is often used over phone calls, where audio quality can vary. Background conversations, network issues, accents, fast speech, and different speaking styles can all affect how accurately a system handles code-mixed speech.
For this reason, businesses should test voice AI using realistic customer conversations rather than relying only on whether the platform lists multiple languages as supported.
How Can Businesses Test a Voice AI System?
Before using a voice AI system for mixed-language calls, businesses should test it with conversations that are similar to what their customers actually say.
1. Test Natural Customer Speech
Use real-life examples that include the languages, accents, and speaking styles your customers commonly use. Do not test only clear and carefully spoken sentences.
2. Test Language Mixing
Check how the system handles two languages in the same sentence. For example, test sentences where Hindi and English words are naturally mixed instead of testing each language separately.
3. Check Important Details
Include names, product names, dates, amounts, order numbers, and other details that the voice AI needs to understand correctly. These details can be important when the system has to take action.
4. Check Understanding and Actions
Do not look only at the transcript. Check whether the system understood the caller’s intent and completed the correct task. For example, if a customer asks to change an appointment, the system should understand the request and take the right action.
5. Test the Voice Response
Listen to the AI’s replies with people who understand the languages being tested. Check whether the response sounds clear, natural, and easy to understand.
Testing should also include realistic call conditions, such as background noise, different speaking speeds, accents, and phone-line issues.
Word error rate (WER) can help measure how many mistakes the system makes while converting speech into text. However, a low error rate does not always mean the system understood the caller correctly. Businesses should also measure intent recognition, important details, task completion, clarification requests, and transfers to human agents.
Conclusion
Code-mixing is a natural way for people to use more than one language in a spoken request. Voice AI must recognize the words, understand how they fit together, and give a clear response. Businesses should check performance with real mixed-language examples and measure whether the caller’s task is completed, not only whether the transcript looks correct.
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