Voice intent analyzer. Upload a clip and AI infers the speaker's underlying intent, motivation, and subtext.
Select the AI model for audio analysis. Different models may have different capabilities.
Record audio directly from your microphone
To work out what someone is really saying, upload a voice clip and the AI separates the message into layers: the surface message (what was literally said), the subtext (what the speaker likely actually means or wants), the emotional state behind the words, and signals of sincerity versus performance. It also identifies tactical cues in the delivery: persuasion attempts, hedging, deflection, and boundary-setting all have audible signatures, and the analysis names which are present. It concludes with the speaker's likely goal in that moment and an honest confidence assessment, including what additional context would raise or lower it. The framing is deliberately candid but humble: tone, pacing, stress patterns, and word choice carry real information about intent, but interpretation is probabilistic, so the analysis presents evidence and reasoning rather than mind-reading certainty. It is a structured second opinion for messages you keep replaying.
Upload the clip (a voicemail, a voice message, a clip from a conversation) and the AI reads it on multiple levels. Surface extraction records the literal content as a baseline. Subtext analysis compares what was said against how: word choices that soften or sharpen, stress placed on revealing words, pauses before loaded phrases, and mismatches between content and tone, the classic marker that the literal message is not the real one. Emotional analysis identifies the state underneath (frustration wearing politeness, anxiety wearing casualness, warmth wearing formality). Sincerity assessment weighs spontaneity cues against rehearsed or performed delivery. Tactic detection flags persuasion patterns, hedging, deflection, and boundary-setting. Goal inference states what the speaker most plausibly wanted from the exchange. The confidence section grades the interpretation and lists what context (history, relationship, the preceding conversation) would firm it up.
Upload the clip and the analyzer separates the layers: the literal message, the probable subtext, the emotional state underneath, and whether the delivery reads sincere or performed. It names tactical patterns it hears (hedging, deflection, persuasion pressure, boundary-setting) and ends with the speaker's likely goal plus a confidence grade on the whole interpretation.
Mismatch detection, mostly. When wording, tone, stress placement, and pauses disagree (a fine that lands flat, a long pause before no problem), the disagreement itself is the signal. The analysis names those specific moments and reasons from them, which is what attentive listeners do instinctively, made explicit and slower.
It is structured inference, and the analysis grades its own confidence and lists what context would change the read: history, relationship, what was said before the clip. Tone carries real information, but a tired person can sound like a cold one and sarcasm misfires without context. Treat it as a second opinion, not a verdict.
No, and be wary of anything that claims otherwise; voice-based lie detection has a poor scientific record. What it can flag is sincerity texture: rehearsed versus spontaneous delivery, hedging, deflection. Those are consistent with many explanations besides deception, which is why findings come phrased as cues and probabilities rather than accusations.
The messages you keep replaying: a voicemail with a strange tone, a voice note that felt off, a clip from a conversation you cannot decode. Ten seconds can carry signal, but more context reads better. One caution: you bring your own bias to ambiguous messages, so let the analysis argue with you, not just confirm you.
Its job is interpretation: what was likely meant, felt, and wanted. That said, the likely-goal section usually implies the response (someone setting a boundary needs acknowledgment, someone hedging needs an easier opening to be direct). Add a note asking for reply suggestions and the analysis will oblige with that framing.
Are they being sarcastic? Upload audio for AI to check if words match tone and flag sarcasm or irony.
What's the sentiment of this audio? Upload a conversation for AI tracking of positive and negative shifts.
How will listeners react? Upload audio and AI predicts emotional responses and flags impact moments.
Do I sound stressed? Upload a voice clip and AI reads stress, cognitive load, and focus indicators.
Are they lying? Upload audio for AI scanning of hesitations and pitch shifts that may signal deception.
Do I sound confident? Upload a recording and AI rates vocal firmness, assertiveness, and certainty.