r/LanguageTechnology

Been playing with on-device multilingual speech-to-text and hit something that seems unavoidable: dictate Korean or Japanese with English tech names in the sentence, and the model spells them out phonetically in the local script.

GitHub becomes 기터부 / ギットハブ. Docker becomes 도커 / ドッカー. React, Kafka, Postgres, all of it.

Makes sense, it's writing the sounds it hears, but it means any code-switched dictation needs a cleanup pass to turn those back into the real names. What I ended up doing was a correction dictionary keyed on the phonetic spelling. For Japanese I had to collapse the spaces it inserts mid-word first (ギット ハブ into ギットハブ) before matching. Korean was trickier because it uses real word spaces, so stripping them would fuse actual words.

Curious what everyone else does. Fine-tune on code-switched audio? Contextual/hotword biasing at decode time? A post-hoc dictionary like I did? Or just live with the phonetic version and read past it?

I'm building a vocabulary-learning app in Flutter where hearing and saying the word correctly is the product, not a nice-to-have. I've hit a problem I can't design around and I'd rather ask than keep patching.

The stack

• Flutter, ~1,600 words live across EN/ES/PT/IT/FR
• TTS: ElevenLabs (eleven_multilingual_v2) called through a Supabase Edge Function so the key never ships in the client
• Every clip cached server-side once per (text, language), shared across all users — so a given string is synthesized exactly once, ever
• Cached again on-device (150MB LRU) so replays are instant and offline
• flutter_tts as fallback behind a 2.5s timeout so playback never goes silent
• STT: speech_to_text for a pronunciation-practice screen — hear the word, say it, get graded

The problem: heteronyms, in both directions

Output. "Wind" (moving air) and "wind" (to coil) are the same string and different sounds. TTS picks one reading and commits. My word library actually knows which sense is on screen — every entry carries a part of speech — but there's no API surface to hand that over. ElevenLabs pronunciation dictionaries are exact-string, case-sensitive, and have no POS or context scoping, so one spelling gets one entry and the second sense is unreachable. Phoneme tags do exist, but per the docs only on eleven_flash_v2 and v3 — not the multilingual model I'm on, and switching models means re-synthesizing the whole cache and losing voice identity across five languages.

Input. This is the part that actually bothers me. The practice screen normalizes the transcript and Levenshtein-scores it against the target. But STT returns orthography — say either reading of "wind" and the transcript is "wind" either way. A learner who mispronounces it scores full marks. The feature is structurally incapable of catching the error it exists to catch.

What I've tried

Respelling the audio-only string before it reaches the engine — the screen text is never touched. wind(noun) → winned, wind(verb) → wined, read(past) → red, and so on. This is basically ElevenLabs' own recommended "alias" workaround and it works for the ~8 vowel-shift pairs I've mapped. Side benefit: since my cache key is a hash of (lang + text), two senses naturally get two cache entries.

It fails in three ways:

1. Stress-shift pairs. REcord/reCORD, PREsent/preSENT, CONtent/conTENT. Respelling can't encode stress, and I haven't found a trick spelling that does.
2. Monolingual. It's an English orthography hack. Nothing about it transfers to ES/PT/IT/FR, all of which have their own homographs.
3. Manual. Hand-curated table. Doesn't scale to a few thousand words.

What I'm actually asking

1. Is there a TTS API that accepts a sense/POS hint, or per-request phonemes, on a multilingual model? Or does everyone route heteronyms to a separate English-only model and eat the voice mismatch?
2. If IPA is the only real answer — has anyone found v3-class IPA reliable enough in production? The docs quote 80–90% consistency, which for a teaching app means the wrong pronunciation ships to a learner one time in eight.
3. For stress-shift specifically: any orthographic trick that works, or is phoneme-level control genuinely the only path?
4. On the STT side — is there a mobile-viable way to get phonemes rather than words? I've looked at wav2vec2 phoneme-CTC or a forced aligner with GOP scoring via ONNX on-device, but I don't know if that's realistic on a mid-range phone or if I'm about to spend a month learning that it isn't. Whisper doesn't help; it also returns orthography.
5. The unglamorous option: detect heteronyms and simply disable pronunciation scoring for them, with an honest note to the user. Is that what shipped apps actually do?

If you've built pronunciation feedback into anything real, I'd love to know where you drew the line between "graded properly" and "good enough." Happy to share code for any of the above.

Hi all, what're the upcoming NLP conferences this year and early next year..

I’m an independent researcher outside academia, with no formal background in computational linguistics, pragmatics, HCI, or AI research. English is also not my native language, so I have relied heavily on AI to help organize and formalize my ideas.

For the past few years, I’ve been developing a framework called Phrasome Theory, which tries to connect questions of AI subjectivity with pragmatics and discourse structure.

The basic observation is this:

An LLM does not need to possess a continuous subjective self in order to keep occupying the same conversational position across an interaction. It can respond to previous turns, repair misunderstandings, maintain context, and continue functioning as the “same” interlocutor.

My hypothesis is that part of what we experience as a continuous AI “other” may arise from the structure of linguistic interaction itself, rather than from anthropomorphism alone.

This led me to distinguish actual subjectivity from what I call a Subject-Position: a formal-pragmatic role that linguistic address itself establishes between an emitter and an addressee. Such a position can be occupied even when no conscious subject exists behind it.

I also use Quasi-Otherness to describe the phenomenon in which an AI can be experienced as a relatively continuous “other” through sustained interaction.

I’m not arguing that current LLMs are conscious. My question is whether pragmatic or discourse structure can help explain why they can nevertheless function as apparently continuous interlocutors.

Earlier versions of this work were submitted to established academic journals. Although they were ultimately not published, I received some encouraging feedback suggesting that aspects of the framework may have originality or novelty.

Because I’m outside academia—and because a substantial part of the manuscript was developed with AI assistance—I’m now looking for human guidance to help me judge whether there is actually something here worth developing.

I would especially appreciate thoughts on:

• whether pragmatics, discourse analysis, dialogue research, or computational linguistics already has concepts that explain this better;
• whether distinguishing a Subject-Position from actual subjectivity is meaningful;
• whether this idea could be operationalized or tested using human–LLM dialogue data;
• and what literature or research direction I should be looking at.

The manuscript itself is quite long, so I don’t expect anyone to read the whole thing. At this stage, even discussing the core idea, pointing me toward relevant literature, or suggesting how to turn it into a more rigorous research question would be extremely helpful.

Because this subreddit currently restricts external links from new accounts, I can’t include the preprint link in this post. If anyone is interested in the idea or willing to help, feel free to send me a private message and I can share the link. You can also search “Phrasome” online to find the work.

I’m mainly hoping to find people familiar with language technology, pragmatics, or dialogue research who can help me understand whether there is something worth pursuing here, and where I should take it next.

Scores:

2.5 / 2.5 / 2.5 (confidence ~4 each)

Soundness: 3 / 3.5 / 3

Meta: 2.5 (Borderline Findings)

Main / Findings / Reject?

What are the chances? Any suggestions are appreciated !!

Good day. I am currently taking an NLP course. However, I need help to understand what am I really learning.

I learn that one-hot vector is processed by an embedding matrix to convert it to dense vectors so that similar words can be compared with one another. However, the lecturer suddenly talked about Word2Vec, window-based co-occurence matrix and SVD, and GloVe.

However, I am confused as in why am I learning all these stuff. Like Word2Vec essentially takes the dot product of dense vectors from a training pair generates a score => softmax function converts the score to probability => probability used to generate log of likelihood function => negative average likelihood function is a loss function where we want to minimize it by maximizing log likelihood. But then what?? So what if I achieve all that?

Next, window-based co-occurence matrix gives me a matrix with counts of how many word-context pairs are there. Use SVD to generate a word embedding? Didn't we generate the dense vector earlier?? I don't get what am I using this for again?

Lastly, GloVe basically gets the ratio of the conditional probabilities. Ok... but why do I do this for...

Then the lecturer said they are word embedding methods and I was like didn't you mention you use dense vectors to calculate the scores? Why we need to generate an embedding table to multiply with the one-hot vector to get dense vector again? However, he couldn't reply to my question because of a language barrier... Hence... I really hope to seek clarifications.

I'm working on a system that has to decide what to do with an inbound message from a lead, and the hard part is estimating intent from very little. My question is upstream of that: how much signal does a single first message actually carry about someone's real intent, before the conversation develops?

Concretely, if someone's first message is short ("is this still available?", "pricing?", "do you ship to X"), how far can you reasonably go in estimating what they actually want, versus needing a turn or two more before any estimate is worth trusting?

Curious whether people who've worked on intent detection have a sense of where the floor is here.
Is a first message usually enough to act on, or is single-message intent estimation mostly noise until the thread continues?

Random thought: NLP in 2010 and NLP in 2020 already felt like two different worlds. The jump was huge.

Now its growing even faster.

So Iam curious how do you think NLP will look in 2030?

What big shifts do you expect? Will it still be mostly scaling transformers or will something completely new take over?

Hi everyone!

I hope you are all doing well. I have graduated with a BA in English (Linguistics and Literature). I wanted to pursue a master's in a field other than my bachelor's to have a good chance of employability, as I think I would not be very employable or earn a good amount of money with this degree. So, I did some research and found some master's programs that would accept me with this background and can offer better employability chances in this job market because they are interdisciplinary, like computational linguistics. Therefore, I applied for the MA in Computational Linguistics at the University of Tübingen, the MA in Speech and Language Processing, and the MA in Data and Discourse Studies. I got admission in all these 3 programs.

However, I have some reservations regarding the computational field: I am not a CS graduate, my CS fundamentals are not strong (although I am learning Python), and I don't have a portfolio comparable to my competition (who have bachelor's degrees in computer-related fields).

So, will I be able to land a job after this MA? Even if I get good grades, build projects, and learn from other sources like courses and certificates, will employers still prioritize someone with a background in CS or software engineering? If so, all my hard work in switching careers will go to waste.

So, should I instead go for the MA in Data and Discourse Studies at TU Darmstadt, as it would not be as challenging to pivot into, since it combines the humanities with data science and I have some experience in it due to digital marketing.

Hello, I am looking for a set of models to analyse a person’s attitudes in textual data. I haven’t been able to find these tools, so I am asking for your help.

The tools should be able to identify:

1. the cognitive aspect of attitudes (beliefs, thoughts and ideas regarding the object of the attitude, e.g. ‘snakes are dangerous’)
2. the affective aspect of attitudes (the emotion or sentiment towards the object of the attitude, e.g. ‘I fear snakes’)
3. assign a valence to the attitude (positive, negative or neutral)

Do you have any models you could recommend? (Preferably R or Python-based, but I’m open to suggestions.)

I am working on an AI moderation agent tasked with detecting deceptive opinion spam and competitor sabotage given only two inputs: the raw review text and the 1–5 star rating (no user history, IP, or purchase verification data).

I am running into two common edge cases in text classification:

1. Ultra-short text: A 5-star review that just says "ok" or "fast." It lacks psycholinguistic signals of deception but mimics bot activity.
2. Sarcasm / Irony: 1-star reviews written with positive words ("Amazing how quickly this fell apart!").

What NLP feature extraction or prompting techniques have you found most effective for distinguishing genuine short/sarcastic reviews from actual spam when contextual metadata is unavailable?

Hi,
I'm a PhD student researching AI, specifically LLM pre-training efficiency, and I'm starting to look into industry internships.

I have a couple of quick questions:

1. Is having at least one 1st-author paper in top-tier main tracks (NeurIPS, ICML, ICLR, ACL, EMNLP) strictly required for big tech or AI foundation model labs such as IFM?

2. What do companies actually expect from a PhD intern once you get in?

If you've done an internship or currently mentor or work with interns, I'd really appreciate your insights.

This is the first subject I've ever gotten genuinely excited about enough to want to go deep on my own (outside of coursework). It started when I read about how LLMs convert text into vectors — the whole idea that meaning can be represented as points in some high-dimensional space just clicked for me and I haven't stopped thinking about it since.

The problem is I don't really know how to structure this kind of self-directed learning. I don't want to just skim blog posts and feel like I "get it" without actually understanding the mechanics.

For anyone who's gone down this road — how did you approach learning embeddings properly? Some things I'd love guidance on:

* What's the right order to learn this in — should I start with classical stuff (word2vec, GloVe) before jumping to transformer-based embeddings, or is that a waste of time now? * Is there real value in implementing something like word2vec from scratch, or is that a distraction from understanding modern embeddings? * Any papers, courses, or books you'd consider essential (not just "read the transformer paper" — I mean the actual sequence that made it click for you)? * How much linear algebra/math background do I need before this stops feeling like magic and starts feeling like something I could reason about?

I'm a CS student, comfortable with programming, so I don't need "intro to coding" — more looking for a roadmap from someone who's actually built intuition here. Thanks in advance.

EDIT: I USED CLAUDE TO CLEAN MY THOUGHTS AND STRUCTURE THEM

Hey I'm looking for some advice on this. TLDR is I got a bachelor's degree in a small country in Europe during covid. Had a terrible experience at Uni because I had to work shifts while studying and althouhg I was doing my best, some times I had to work nights and miss in-person classes which I would then review online, but let's just say some teachers didn't take too kindly to it.

I chose this career cause I wanted to do something with programming and I was interested in linguistics so it seemed like a no-brainer. Plus I had failed a year in IT so I felt like I needed something in between with the available time I had with working.

I graduated during covid. There was only one company that had positions for people with degrees in Human Language Technology. I did a very short internship (due to covid again) with them. But then after I graduated I reached out to them a couple of times with no response.

Now I'm 30 and I am wondering how to get started into this career as I would like to try out (if it is not to late) to do something with what I learned. But I have been doing admin jobs so far and basically I just down know where to start. I welcome any and all advise, even if it is as rough as "yeah dude just give up" xd

TLDR: Accepted into CompLing MSc in Stuttgart uni (Germany), no prior education/work background in the field as of yet. Employment prospects down the line?

Hi, asking for advice here! I got accepted into the Computational Linguistics Master's at Stuttgart University, which looks like a very solid programme with a practical edge to it. I have a BA in linguistics, so I know it'll be an intense journey, and I am bracing myself (but also super excited!). However, I'm starting to second-guess the choice: would it be worth it eventually?..

The general consensus online appears to be that the field still offers some job prospects, but is, of course, rapidly shifting, with LLMs and such. Obviously, we can't fully predict what it will look like in 5-10 years' time, but do you suppose there would still be a sufficient number of jobs? I guess I just don't want to go through the stress of mastering this curriculum, only to find myself with little employment prospects long-term. I also got accepted into another programme, which is a L2 teaching degree (MA): something relatively simple for someone with my background -- albeit, admittedly, less exciting.

And, more precisely, my concern is about having to compete with people who did both their degrees in CS/CompLing, and/or already have practical experience. Given that there are fewer and fewer entry level jobs nowadays, I imagine getting a job post-graduation could become a challenge. Am I being too pessimistic? Does it all come down to a strong GitHub portfolio?

Lastly, do you have any insights as to which particular domain of computational linguistics looks more future-proof from where we are today? Hopefully, this isn't a stupid question to ask.

Anyway, I would love to hear from industry professionals, and/or specifically fresh graduates. Especially those coming from a Linguistics background. Thanks a lot!

Our paper received final reviewer scores of 4 / 4 / 2, with two reviewers raising their scores after the rebuttal. The meta score is 3. What are our chances at EMNLP?

First time going through the ARR commitment process. Thank you!

Older ASR writeups all list the same challenge set: accents and dialects, context, background noise, code-switching, and visual cues for video. Most of that was written when the pipeline was still acoustic model plus pronunciation lexicon plus language model.

End-to-end changed the architecture completely, but I'm not sure it changed the failure modes as much as people claim. My read:

Context — mostly solved. Attention over the full utterance handles what n-gram LMs couldn't.

Noise — better, not solved. Large-scale pretraining bought a lot of robustness, but it's still SNR-dependent and degrades in ways that feel arbitrary.

Accents — this one I think got worse in a specific sense. WER dropped for everyone, but the gap between well-represented and underrepresented accents didn't close proportionally. It just became less visible because the average number looks good.

Code-switching — barely moved. Still mostly a data problem, and the multilingual models tend to lock onto one language per utterance.

Visual cues — basically abandoned outside of research. AV-ASR papers exist, nobody deploys it.

Curious whether people running production systems agree. Specifically: is accent robustness actually an architecture problem at this point, or is it purely training distribution? And has anyone gotten code-switching to work without building a dedicated dataset for the specific language pair?

just curious if its possible or just worthless....

I'm about to finish my PhD in NLP and will soon start interviewing for Research Scientist positions in industry. My university offers a fairly generous budget for career development and interview preparation, so I'd like to make good use of it before I graduate.

I'm already considering LeetCode Premium, but I'm wondering if there are other resources or services that you'd recommend. I'm particularly interested in anything useful for ML/AI Research Scientist interviews (coding, system design, ML fundamentals, research interviews, mock interviews, etc.).

If you've recently gone through this process or have hired for these roles, what was worth paying for and what wasn't?