Words tell you what someone said; tone tells you how they meant it. This speech emotion recognition tool listens to a recording and reports the overall sentiment (positive, negative or neutral), the specific emotions it hears, such as happiness, frustration, sadness, anger or anxiety, and how strongly each one comes through. It combines the content of the speech with how it is delivered: pitch, pace, loudness, tension and pauses. Because moods move, the report includes a timeline with timestamps showing where the tone shifts, a confidence level for each reading, the context that may explain it, and suggestions for how to respond. Use it on calls, voice messages, interviews or your own practice recordings.
Example result
Input
A 4-minute customer voicemail about a delayed delivery, left on a company support line.
Output
Overall sentiment: negative, moderate intensity. Emotional timeline: 0:00 to 0:40, neutral and polite. Even pace, clear explanation of the order. 0:40 to 2:10, rising frustration. Pitch climbs and words become clipped when the caller mentions a second missed delivery date. Confidence: high. 2:10 to 3:20, anxiety. Faster speech and audible sighs while describing the gift being needed by Friday. Confidence: moderate. 3:20 to 4:00, resigned but cooperative. Volume drops, and the caller leaves a callback number calmly. Contextual factors: the frustration is aimed at the process, not a person, and the deadline drives the anxiety. Recommended response: call back promptly, acknowledge the missed dates first, and lead with a firm delivery date rather than an apology alone.
Scores
How it works
How Sentiment Analyzer works
- 01
Add a recording
Sign in, open the sentiment analyzer and upload an audio file or record in your browser. Conversations, monologues and voice notes all work.
- 02
Reading delivery and content together
The AI considers what is being said alongside acoustic signs of emotion: a raised, tight pitch, quickening pace, clipped words, sighs, long pauses or a flat, low-energy voice.
- 03
Tracking changes over time
Rather than one label for the whole clip, it follows the emotional arc and marks timestamps where sentiment turns, for example from neutral to irritated, or from anxious to relieved.
- 04
Scores, confidence and advice
You get sentiment and intensity scores, a confidence note on each emotion, the contextual factors that might be driving it, and recommendations for how to respond or follow up.
Use cases
Who it is for
Support and service teams
Review calls or voicemails to see where a customer's mood turned and what triggered it, then use that moment in coaching.
Researchers and UX teams
Run interview or usability session recordings through the tool to flag moments of delight, confusion or irritation that the transcript alone would miss.
Checking your own tone
Record a difficult message before you send it, such as feedback to a colleague, and hear whether it comes across as calm and constructive or sharper than you intended.
Content creators and voice actors
Confirm that a scene or episode lands with the emotion you planned, and see where the energy dips across the timeline.
Tips
Get better results
- Use the clearest audio you have; heavy compression, music or crosstalk can blur the pitch and energy cues the AI depends on.
- Include enough context. A single sentence can sound angry that a full minute would show was just emphatic.
- For conversations, note who is speaking when you read the timeline, since the report follows the overall emotional flow of the recording.
- Pay attention to the confidence notes. Low-confidence readings are a prompt to listen again yourself, not a conclusion.
- Compare several calls of the same type to spot patterns instead of drawing conclusions from one recording.
Limitations
What it can't tell you
Emotion recognition from speech is an estimate, not a reading of someone's inner state. People express feelings differently across cultures, languages and personalities, and a raised voice can mean excitement as easily as anger. Sarcasm, accents and poor audio can mislead the analysis. Do not use the results as the sole basis for judging someone's mental health, honesty or performance, and ask people's permission before analyzing recordings of them.
Frequently Asked Questions
Sentiment Analyzer questions
What is speech emotion recognition?
Speech emotion recognition is the task of identifying emotions from spoken audio. Rather than only analyzing the words, it also listens to how they are said, including pitch, pace, loudness and voice quality. This tool uses a multimodal AI model to do that and returns sentiment, named emotions, intensity and a timeline of changes.
How is voice sentiment analysis different from text sentiment analysis?
Text sentiment only sees the words, so "that's just great" reads as positive. Voice sentiment analysis also hears the delivery, so a flat, drawn-out "great" can be flagged as sarcastic or frustrated. Combining both gives a more realistic picture of how the speaker actually feels.
Which emotions can it detect from voice?
It commonly reports happiness, sadness, anger, frustration, anxiety, calm, excitement and neutral tone, plus the overall positive, negative or neutral balance. The report names whichever emotions it hears in your clip and rates their intensity, so you will not see a fixed list every time.
Can it analyze a conversation with several people?
Yes, it will describe the emotional flow of the whole recording and mark where the tone shifts. It does not label each speaker separately. If you need to know who spoke when first, run the clip through the speaker diarization tool and then read the sentiment timeline alongside it.
Is this a tone of voice analyzer I can use for practice?
Yes. Record yourself delivering a message and the report tells you what emotions a listener is likely to hear, where your tone shifts, and how intense it sounds. It is a practical way to check whether you sound as calm, warm or firm as you intend.
Is sentiment analysis part of a plan?
It is one of the AI tools available with a paid LindaleAI subscription. Each recording analyzed uses credits from your monthly allowance, and the pricing page explains the options.
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Sentiment Analyzer uses credits from a paid LindaleAI plan. See pricing