How accurate is a Malayalam voice bot on real phone calls?
No honest percentage exists, because accuracy shifts with the line, the speaker and the question. A Malayalam voice bot is more reliable on short, closed questions in a quiet place and less reliable on long fused words, numbers, names and weak signal. Measure it on your own calls by checking captured fields against recordings, rather than trusting any vendor's figure.
Why one accuracy number misleads
Word-level accuracy and call-level accuracy are different things. A transcript with a few wrong words can still capture the right appointment day, while one wrong digit ruins a mobile number. What matters to a business is whether the details it acts on are right.
What makes Malayalam calls harder
- —Quick speech in which suffixes pile onto roots, so one dropped syllable can change a whole phrase.
- —The ഴ sound, which can come out as ള or യ in a poor transcript.
- —Amounts said in mixed ways, such as pavan, cents, 'ഒന്നര ലക്ഷം' or plain English figures.
- —Background noise from a bus, a market or a Gulf call echoing on a speakerphone.
- —Weak signal in hill and coastal pockets, where audio breaks up mid-word.
A scorecard to run on 50 calls
| Field | How to check it | Why it matters |
|---|---|---|
| Mobile number | Compare the captured digits with the recording | One wrong digit loses the lead |
| Name and place | Listen for long names and place names | Wrong spelling breaks CRM matching |
| Date and time | Check the booked slot against what the caller said | A wrong day causes a no-show |
| Amount and unit | Look for lakh, pavan and cents errors | Units drive the quote |
| Outcome | Compare interested, callback or not interested with the call | It decides who your team calls next |
Count right answers over total calls for each field, and score pure Malayalam calls and Manglish calls separately. The gap between the two is often the useful finding.
What reduces errors in practice
Region matters too. Run the scorecard separately for Malabar, central Kerala and southern callers, and again for older and younger speakers, so that a weak result in one group is visible instead of being averaged away.
Read-backs of numbers and dates, short one-idea questions and a clean knowledge base help most. Every Outpero call is recorded, transcribed and summarised, with the captured fields beside it, so an error can be traced to the line or to the conversation. No accuracy percentage is published, and that is deliberate.
Want to hear an AI voice agent handle this on a real call?
Follow-up questions
Which should I trust when they differ, the transcript or the recording?
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Trust the recording. A transcript is a machine's reading of the audio, and it can drop or swap a syllable. When a captured field looks wrong, listen to that part of the call and correct the script or the question.
Does the bot say when it has not understood?
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It can be instructed to ask the caller to repeat when speech is unclear and to read key details back. No agent hears every call perfectly, so add a confirmation step for anything important, such as a date, an address or an amount.