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> Try asking any of them “Am I speaking in a low voice or a high voice?” in a high-pitched voice, and they won’t be able to tell you. I wonder how much of that
by miki123211 1y ago
> Try asking any of them “Am I speaking in a low voice or a high voice?” in a high-pitched voice, and they won’t be able to tell you.
I wonder how much of that is LLMs being bad, and how much is LLMs being (over) aligned not to do it.
AFAIK, Chat GPT Voice mode had to have a lot of safeguards put on it to prevent music generation, accent matching (if you sound Indian, it shouldn't also sound Indian), and assuming ethnicity / biasing based on accents.
It doesn't seem that impossible to me that some of these behaviors have been aligned out of these models out of an abundance of caution.
- sbrother 1y agoI don't think it's just safeguards; they really don't seem to understand pitch at all. I tried asking ChatGPT's advanced voice mode to recognize a tune I was humming, and it insisted it was Beethoven's 5th -- multiple times. I think it must have basically tokenized my humming to "dun dun dun duuun".
- bigzyg33k 1y agoadvanced voice mode operates on audio tokens directly, it doesn't transcribe them into "text tokens" as an intermediate step like the original version of voice mode did.
- cubefox 1y agoBut they behave just like models which use text tokens internally, which is also pointed out at the end of the above article.
- bigzyg33k 1y agowe don't know if that's due to inherent limitations of the tokenisation of audio, or a byproduct of reinforcement learning. In my own usage, I noticed a significant degradation in capabilities over time from when they initially released advanced voice mode. The model used to be able to sing, whisper, imitate sounds and tone just fine, but I imagine this was not intended and has subsequently been stunted via reinforcement learning. I don't find the articles argument that this is due to tokenisation convincing.
- cubefox 1y agoThey didn't say it's due to tokenization. > This is likely because they’re trained on a lot of data generated synthetically with text-to-speech and/or because understanding the tone of the voice (apparently) doesn’t help the models make more accurate predictions.
- sbrother 1y agoright, but either whatever audio tokenization it's doing doesn't seem to encode pitch, or there's ~nothing where pitch is relevant in the training set.
- oezi 1y agoAbsolutely correct! My simple test is if it can tell American and British English Tomato and Potato apart. So far it can't.
- fragmede 1y agoWhich "it" are you referring to? There are models that can.
- idonotknowwhy 1y agoQwen3 omni transcriber can do this. It can describe the voice, emotion very well
- 85392_school 1y agoI've also had luck with Gemini. If I made a few noises and asked which one was higher pitched, it could easily tell.
- tsol 1y agoDid they respond differently depending on what race they thought you were? I'm surprised they would even do that honestly. I thought they were trained on text conversations which presumably wouldn't have any of that to learn from.
- OisinMoran 1y agoYou can often tell where someone is from from text alone! There are plenty of idiosyncrasies even in how different English speaking countries use the language.
- anotherhue 1y agoAh stop
- fragmede 1y agoLike, what do you mean? Are there, like, particular mannerisms that people from some regions that are hella unique to those regions?
- robotresearcher 1y agoI say old chap, what colour are your mummy’s wellies?
- ctxc 1y agoClever!
- deleted 1y ago[deleted]
- ElevenLathe 1y agoYou betcha!
- xwolfi 1y agoAll my Indian colleagues say "I agree with the same", this "the same" turn of phrase was so strange to me I had to ask (I'm French, so I have my own silly quirks, like I forget non-vocal plural(s<-- see, often I don't write that s)). They told me it was like that in Hindi so they just reproduce the pattern and it's grammatically acceptable. For French people like me, false friends are immediately noticeable: for instance, "actually" to mean "now" instead of "in fact".
- deleted 1y ago[deleted]
- vvolhejn 1y agoAuthor here. I think it's more of a capability issue than a safety issue. Since learning audio is still harder than learning text, audio models don't generalize as well. To fix that, audio models rely on combining information from text and audio (having a single model that consumes/produces both text and audio tokens) and the audio tokens basically end up being an integrated speech-to-text/text-to-speech. This reflects my colleagues' experience working on Moshi, and it seems to be the case for other models too, see the Conclusion section. Part of the reason can also be synthetic data: if you fine-tune on data generated from text via a text-to-speech, the tone of the voice doesn't have any information, so the model learns to ignore it.
- j45 1y agoAccent detection or consciously ignoring it is a filter step.
- JoshTriplett 1y agoAudio models for speech not understanding pitch, seems similar to how text LLMs often don't understand spelling: it's not what they were trying to recognize.
- oezi 1y ago> generated from text via a text-to-speech Yes, frustratingly we don't have good speech-to-text (STT/ASR) to transcribe such differences. I recently finetuned a TTS* to be able to emit laughter and hunting for transcriptions which include non-verbal sounds was the hardest part of it. Whisper and other popular transcription systems will ignore sigh, sniff, laugh, etc and can't detect mispronounciations etc. * = https://github.com/coezbek/PlayDiffusion https://github.com/coezbek/PlayDiffusion
- jasonjayr 1y agoIIRC -- the 15.ai dev was training on fan-made "My Little Pony" transcriptions, specificaly because they included more emotive clues in the transcription, and supported a syntax to control the emotive aspect of the speech.
- bongodongobob 1y agoHmm, the last time I played with GPT voice mode it was able to do all kinds of different accents.
- phrotoma 1y agoLike others I noticed this capability was interfered with in some way. I had fun getting it to speak to me in a cheesy over-the-top Bostonian accent early on, then one day when I tried to demonstrate for a friend it interrupted itself mid-sentence, literally one voice speaking over the other truncated voice, saying something like "I'm sorry I can't mimic voices". It seemed like they had one model monitoring the output of another model and then cutting it off when it crossed some line.
- userbinator 1y agoaccent matching (if you sound Indian, it shouldn't also sound Indian) Why not? I've found that it helps greatly with mutual intelligibility when both sides are speaking a similar dialect, and the one who can do this switching, switches to that of the one who can't. (I wish I could also use an Indian accent bidirectionally; would definitely come in handy for those aggravating times I've had to talk to an outsourced customer service department.)
- philipallstar 1y agoThis isn't a logic thing, it's a politeness law invented by white liberal western women thing.
- xp84 1y agoExactly this. It fits that worldview to think a literal computer was “being racist” and mocking the user, even just by copying their speech patterns accurately.