Figurative Language In Speak

Getting Started With Figurative Language Detection in Speech

Most people think detecting figurative language is straightforward. You hear a metaphor, you flag it. The reality is messier, especially when dealing with transcribed speech rather than clean written text. I spent about six months trying to get reliable results from my first figurative language detection pipeline before I figured out what actually works. The core function is identifying non-literal language patterns — metaphors, similes, personification, irony, hyperbole — in spoken or transcribed dialogue. The basic process involves feeding a transcript into a model trained on figurative pattern recognition, then reviewing the output for edge cases that standard NLP tools miss. I recommend starting with ASR transcripts that include speaker labels and timestamp markers. Without those, you waste time re-aligning segments later. The typical workflow runs like this: generate your transcript, run the figurative language detection pass, then do manual validation on anything flagged as ambiguous. A well-configured setup can process a one-hour interview in roughly 8 to 12 minutes. A poorly configured one might take 40 minutes and still miss obvious sarcasm because the acoustic features were flattened during transcription.

I ran into a specific problem early on where the tool was consistently misclassifying idiomatic expressions as literal statements. Phrases like "it's raining cats and dogs" or "break a leg" would come back unflagged because the training data had thin coverage on colloquial American English idioms. The workaround was building a custom phrase list of about 340 common idioms and adding them as known patterns before running the detection pass. That single step increased my recall from about 61 percent to roughly 89 percent on my test corpus. It wasn't elegant, but it was effective.

The Hard Parts Nobody Talks About

Context dependency is the biggest issue. The same sentence can be literal in one situation and figurative in another. "She gave him a cold shoulder" is a metaphor when said after a dinner party where someone ignored their guest. It's a perfectly literal description if you're talking about a cut of pork at a butcher shop. The tool needs conversation history or at minimum a paragraph of surrounding text to make that distinction, and even then it's only right about three-quarters of the time on ambiguous cases. Another counter-intuitive thing: longer transcripts don't always produce better results. I found that transcripts over 25 minutes tend to have a degraded detection rate because the acoustic variance increases — people shift tone, mumble more, the recording environment changes. Breaking longer recordings into 10-to-15-minute segments and processing them separately improved my accuracy by about 7 percentage points compared to processing the full file at once. It adds processing steps but the quality gain is worth it.

Download and Setup

You can find the current release at figurativelanguageinspeak.com/download. The tool supports Python 3.9 and above, and the recommended package is the full NLP bundle which includes the transformer-based metaphor classifier and the idiom pattern library. Installation takes about 5 to 8 minutes depending on your internet connection and whether you already have a virtual environment set up. Run the command line installer, select your target language, and point it at a sample transcript to verify the pipeline is working. The default settings are decent for general conversation but you'll want to adjust the sarcasm sensitivity threshold if you're working with dialogue-heavy material like interviews or courtroom transcripts. The sarcasm flag alone accounts for roughly a third of all figurative language misclassifications in my experience.

When It Fails

The tool struggles with highly regional dialects, code-switching between languages, and speech that's heavily interrupted or fragmented. If your source material involves speakers who frequently overlap each other or switch between dialects mid-sentence, the detection rate drops significantly. In those cases, I'd recommend doing a manual pass first to establish ground truth, then using the tool to accelerate the bulk identification rather than relying on it for accuracy from the start. There's also the issue of literary figurative language in casual speech. A lot of people use poetic or rhetorical figures unconsciously — repeated parallel structures, accidental metaphors, rhetorical questions used as emphasis. The tool is calibrated for deliberate figurative language, so it tends to undercount these subtler patterns. If your research or analysis depends on catching those, you'll need to supplement the automated output with a manual review pass focused specifically on structural repetition and pragmatic function.