Most text to speech systems have never been designed with India in mind and that one fact accounts for a great deal. A voice bot contacting a customer in Hindi, Tamil or Telugu should sound as if it’s part of a natural conversation. Instead it sounds as though a machine is reading from a script in an accent which no one actually uses; the pronunciation becomes robotic, Hinglish is produced in a flat manner, and all the different dialects of the country are squeezed into a single, generic Hindi accent. ConvoZen’s Rani TTS is one of the more convincing efforts to correct this problem since it has been developed taking into account Indic phonetics and the way that people in urban India actually switch between languages in the middle of a sentence, sometimes doing so twice, without warning. So why has this issue remained so persistent, and what makes Ragini different?
The Problem: Why Text to Speech AI Struggles With India’s Language Layer
India has 22 scheduled languages as well as hundreds of dialects; in the cities people practise code-switching in the way they speak.
For example: speaking Hindi-English, Tamil-English, or Telugu-English and this tendency also applies to the way they text and the way they expect to be spoken to.
Most text to speech AI engines fall short here in ways you can predict before you even test them:
- Incorrect stress and intonation when pronouncing Indic scripts.
- The pronunciation of English loanwords has become stiff when incorporated into an Indic sentence.
- The change in language at that moment is jarring.
- A dialect levelling which causes a caller from Lucknow to sound the same as one from Chennai.
- For a support bot or when making a sales call, none of this is merely decorative; it determines whether the person on the other end trusts the voice or hangs up.
Why Hindi, Tamil and Telugu Specifically Trip Up TTS Engines
The Indic deployments usually fail at two places; once you’ve sorted out those, the rest becomes much simpler.
Script and Phonetic Complexity
Unlike the Latin script, which uses letters individually, the Indic scripts are syllabic and phonetic, consisting of consonant-vowel units. Since most text-to-speech models are initially trained and tuned on data written in the Latin script, their performance drops considerably when they encounter Devanagari, Tamil or Telugu, with the stress falling on the incorrect syllable and the rhythm becoming flat.
Code-Switching
What I’m seeing here isn’t a translation; it’s mid-sentence switching, which can occur twice within a single sentence. English has about 44 phonemes while Hindi has around 52, including retroflex consonants that English doesn’t have. Fonada Labs. Most models use monolingual pipelines, so they either pronounce the English word using Hindi phonetics or fail to make the switch and instead give a flat, lifeless reading that at once alerts the listener to the fact that it’s a machine. Dialect and accent levelling is the most difficult of all the layers, and any company claiming to handle every dialect in the same way should certainly raise suspicion.
How Ragini TTS Approaches This Differently
Ragini has been trained and fine-tuned for Indic phonetics and for speech that code-switches between Hindi and English. It is not a Latin-script model with a translation layer added to it. Both Ragini and ConvoZen’s Akshara speech-recognition model were developed using more than 50,000 hours of real telephonic data from India, providing native support for nine regional languages including Bengali, Gujarati and Malayalam, without relying on English-centric translation methods by ConvoZen. The system is also designed with telephony in mind and has been tuned to work within the 8 kHz bandwidth characteristic of Indian telecom networks – the same conditions under which global ‘HD-only’ models usually perform poorly.
ConvoZen is just as open about this point: although support for Hindi, English and the switching between Hindi and English is already strong, assistance with other languages is still in the process of improvement. This is a much more realistic statement than the typical claim that all languages have already been solved.
RaginiLite – Built for Volume, Not Just Quality
The easy part is getting a single well-worded sentence right; the real engineering challenge is generating millions of short voice messages during thousands of simultaneous calls without the conversation being severely affected by latency. That’s why RaginiLite was designed for this purpose.
In a test involving 50 requests across Hindi, English and Hindi-English code-switched messages, Ragini achieved a real-time synthesis speed of 118 times on the GPU and about 16 times on the CPU, with a median GPU response time of approximately 112ms and an audio output rate of 22.05kHz. It has also been demonstrated to run at 6 times real-time on a Raspberry Pi despite having only 2GB of RAM.
At the level of call centre operations, those figures are important. A support bot which is producing its replies in real time cannot afford to hesitate for even a second. A median response time of 112ms ensures that the conversation remains ongoing rather than becoming a recorded message.
Where This Actually Gets Used
- Voice agents used for sales and customer support – this involves outbound calling that adjusts to different dialects and first-line support bots which respond in the customer’s own language.
- With no-code deployment, teams are now able to set up a voice agent or a WhatsApp voice campaign using just a single prompt or command line, without the need for a dedicated voice engineering team.
- For banking and finance; premium reminders, EMI due dates, and account enquiries are all dealt without the need for a human agent on each call.
- Agents in the field carry out calls to follow up on enquiries about properties, arrange site visits and keep up with any documentation that is overdue.
- Healthcare includes sending appointment reminders and carrying out patient follow-ups in a conversational manner, and only involves a human when this is truly necessary.
Conclusion
The global issue with regional languages in voice AI has not been resolved, and companies asserting that it has are merely overstatement. The change lies in the approach: it is now phonetically tuned, aware of code-switching, and a high-throughput text-to-speech system designed around India’s real language mix rather than one that has been adapted from English. Ragini TTS serves as a good example of this shift.
When looking at different options, ignore the demo sentence and request throughput and code-switching metrics that are suitable for your particular use case. For further information regarding ConvoZen’s approach, go to convozen.ai.
Frequently Asked Questions
Q1. What makes Indian languages such a difficulty for TTS systems?
Ans: Most of them are based on data written in the Latin script and assume English-style pronunciation. The syllabic structure of the Indic scripts, along with continuous code-switching, brings out the weaknesses in pronunciation, stress, and rhythm.
Q2. What counts as code-switching and why is it important for voice AI in India?
Ans: It involves using two or more languages in the same conversation or sentence – for instance, Hinglish. With text-to-speech technology, the system must detect the language change, apply the correct pronunciation rules, and maintain a natural prosody so that the output doesn’t sound as if it’s made up of two separate voices.
Q3. What is meant by “118× real-time”?
Ans: It refers to the ratio of synthesis speed to the length of the audio. Under the above test conditions, RaginiLite generated speech at a speed much greater than the speed at which it was played back. In the case of live voice agents, however, synthesis speed is only one factor; it is the latency and the time taken to produce the first audio signal throughout the rest of the system that determine how quickly a caller hears a reply.
Q4. Is the Hindi-English text-to-speech system any different from having separate Hindi and English models?
Ans: Yes, and this is the point at which most demonstrations mislead. Simply generating the two languages separately does not ensure a natural transition between them; what actually matters is language-aware pronunciation and intonation within the same sentence-this is why tests using code-switching provide more useful information than demonstrations that use only one language.
The cost of running TTS on a large scale in India differs according to the way it is deployed. The actual economic situation will depend on the model used, the hardware, the length of the audio, the level of concurrency, the telephony infrastructure and the pricing. The only thing that can be stated with confidence is that large-volume deployments gain from faster synthesis and smaller infrastructure requirements, since compute efficiency is indeed a significant cost when you are dealing with thousands of short interactions.
