7 ms·
Ask HN: How to boost Gemini transcription accuracy for company names?
I’m using Gemini for speech-to-text and it often misrecognizes company names and acronyms.
Is there any way to use a custom lexicon or vocabulary with Gemini to improve recognition accuracy?
If not directly supported, what are practical workarounds people use — e.g. preprocessing prompts, fine-tuning, or combining Gemini with another ASR that supports phrase boosting?
- gallexme 11mo agoAdding it to the instructions worked well for me with specific terms
- semessier 11mo agoadding to the question, ruling out fine-tuning for practicality, what about injecting names towards the embedding but not into the context?
- lysecret 11mo agoI generally found 4o-transcribe to be more performant than gemini fyi.
- Reubend 11mo agoAny company names or special acronyms should be added to your prompt.
- tifa2up 11mo agoDon't solve it on the STT level. Get the raw transcription from Gemini then pass the output to an LLM to fix company names and other modifications. Happy to share more details if helpful.
- remus 11mo agoI've had some luck with this in other contexts. Get the initial transcript from STT (e.g. whisper), then feed that in to gemini with a prompt giving it as much extra context as possible. For example "This is a transcript from a youtube video. It's a conversation between x people, where they talk about y and z. Please clean up the transcript, paying particular attention to company names and acronyms."
- samtts 11mo ago[dead]
- flyinglizard 11mo agoI've done the same, it works very well.
- idopmstuff 11mo agoYeah, I've done it with industry-specific acronyms and this works well. Generate a list of company names and other terms it gets wrong, and give it definitions and any other useful context. For industry jargon, example sentences are good, but that's probably not relevant for company names. Feed it that list and the transcript along with a simple prompt along the lines of "Attached is a transcript of a conversation created from an audio file. The model doing the transcription has trouble with company names/industry terms/acronyms/whatever else and will have made errors with those. I have also attached a list of company names/etc. that may have been spoken in the transcribed audio. Please review the transcription, and output a corrected version, along with a list of all corrections that you made. The list of corrections should include the original version of the word that you fixed, what you updated it to, and where it is in the document." If it's getting things wrong, you can also ask it to give an explanation of why it made each change that it did and use that to iterate on your prompt and the context you're giving it with your list of words.
- dotancohen 11mo agoWhich specific model do you use?
- huflungdung 11mo ago[dead]
- simonw 11mo agoHave you tried feeding it a system prompt with a list of custom vocabulary? I would expect that to work really well. "Transcribe this audio. Be careful to spell the following names and acronyms right: list-goes-here"
- samtts 11mo ago[dead]
- gearhart 11mo agoWe use openwhisper for transcription which accepts a list of "words to look out for" which we populate with a short list of the names of all the people and companies most likely to be mentioned in the text, and then we do a spell checking pass at the end using Gemini with a much longer list, telling it to look out for anything that might be a misspelling. It's not perfect, but it's taken it from being an issue that made all our transcripts look terrible, to an issue I no longer think about. I imagine just using the second spellchecking pass with Gemini would be almost as effective.
- vayup 11mo agoSomething along these lines, as part of the prompt, has worked for me. # User-Defined Dictionary Always use the following exact terms if they sound similar in the audio: ```json {{jsonDictionary}} ```
- another_twist 11mo agoUse any proper ASR service that supports custom vocabulary ? Transcribe and Deepgram definitely support it and if you want to go fancy Nemo with custom vocabulary. Are there constraints where you have to use Gemini ?
- rancar2 11mo agoThe business edition of Wispr Flow does this well, and includes sharing among teams so you can make sure that the company wide vocabulary is consistent and well recognized. https://wisprflow.ai/business https://wisprflow.ai/business
- e1g 11mo ago+1 from another happy Whispr Flow power user. I tried 4-5 similar apps and even built one with Assembly AI, but Whispr is a significant upgrade above the rest for correctly recognizing my accent and jargon. Having the custom vocabulary helps.
- alex-skobe 11mo agoWe have used markdown and list of vocabulary at the end like Return company name only from dictionary #dictionary 1:Apple 2:.. And than Vercel AI sdk + Zod Schema + Gemini 2.5 pro and it pretty accurate
- meerab 11mo agoI use a two-pass approach - first pass with ASR (OpenAI Whisper) and second pass with an LLM. I ask users to provide context upfront and use that as the "initial_prompt" parameter in Whisper: https://github.com/openai/whisper/discussions/963#discussioncomment-4987057 https://github.com/openai/whisper/discussions/963#discussion... Gemini might have similar capabilities for custom vocabulary, though I'm not certain about their specific implementation. The two-pass ASR+LLM approach could work with Gemini's output as well.
- koko12 11mo ago[flagged]
- b112 11mo agoGive it a database backend with lots and lots of facts. Things verified by humans. There, AI 'fixed'.
- brokensegue 11mo agoI don't get your suggestion. How does the database tie into speech to text?
- halobcaklik 11mo ago[flagged]
- halobcaklik 11mo ago[flagged]
- deleted 11mo ago[deleted]
- mediaman 11mo agoWe do this simply by injecting a company-defined list of proper names/terms into the prompt, within <special_terms>, and telling it to use that information to assist with spelling. It works pretty well.
- gawi 11mo agoIf you are able to isolate the text portion corresponding to the company name, you can compute the similarity (based on the character edit distance - Levenshtein) against every item of a predefined list of companies (and their aliases) and pick the best match.
- wanderingmind 11mo agoThere was a paper that tried to integrate NER (Named Entity Recognition) with whisper to one shot for similar situation, not sure what is the current status [1] https://github.com/aiola-lab/whisper-ner https://github.com/aiola-lab/whisper-ner