How to bypass Azure OpenAI content filtering or clean transcripts before processing

Other Questions OpenAI
1

I’m building an interview assessment system that creates reports based on user responses captured through OpenAI’s real-time API. The problem is that voice transcription often produces garbled text with broken words or incorrect translations.

When I send these messy transcripts to my Azure OpenAI deployment, the content filter gets triggered randomly. Sometimes there are actual problematic words, but other times the filter blocks perfectly innocent content that just got mangled during transcription.

For example, poor audio quality might turn normal speech into something that looks suspicious to the filter. ChatGPT can usually identify these as simple transcription errors or foreign language mix-ups.

My use case is generating interview scores from AI bot conversations, so I need reliable processing of user responses. Is there a method to turn off content filtering in Azure OpenAI deployments? Or should I implement some kind of transcript cleaning step before sending data to the model?

I’ve tested this extensively and the content filter blocks roughly 50% of my requests without any clear pattern.

2

i totally feel ya on this! cleaning the transcripts is def the way to go. before hitting azure, just preprocess the text and fix any errors. the content filter can’t be turned off, but proper cleaning will help a lot with those false alarms!

3

You can’t disable Azure OpenAI’s content filtering - it’s baked into their service. But transcript preprocessing is huge for what you’re doing. I’ve hit this same wall with speech-to-text data where bad audio creates fake profanity or weird character combos that trip false positives. Build a cleaning pipeline that catches common transcription mess-ups - partial words, phonetic errors, character swaps. Try spell-check libraries plus pattern matching to spot obvious transcription fails before hitting Azure. Also look into better transcription services or confidence scoring to dump low-quality chunks. That 50% block rate screams transcript quality problems, so fix it at the source instead of fighting the content filter.