Concept guide
Voice sentiment analysis vs emotion recognition
“Voice sentiment” often combines several different systems. Transcript sentiment reads the words. Speech emotion recognition reads the delivery. A useful call or voice product usually needs both—and must keep their disagreement rather than flattening it into one score.
The four layers
| Method | Input | Output | Best question | Blind spot |
|---|---|---|---|---|
| Transcript sentiment | Recognized words | Usually positive, neutral, or negative | What attitude do the words express? | Tone, pacing, hesitation, laughter, and ironic delivery |
| Speech emotion recognition | Acoustic signal | Emotion-related scores such as frustrated, happy, or worried | How does the delivery sound? | Conversation history and the literal meaning of the words |
| Speaking-style analysis | Acoustic signal | Styles such as hesitant, confident, sarcastic, or monotone | What manner of speaking is acoustically present? | Whether the apparent style matches the speaker’s intention |
| Contextual intent model | Words, acoustic scores, metadata, and dialogue state | Application-specific events or intents | What should this product do next? | Only as good as its domain labels, policy, and context window |
Words positive, voice strained
“Sure, that’s totally fine.”
The transcript may look positive. A strained voice, long pause, clipped timing, or sarcastic style can provide contrary evidence. The correct output is not to overwrite one channel; it is to preserve the disagreement for the application.
Words negative, voice playful
“You’re impossible.”
Text alone may label the sentence negative. Laughter, timing, shared history, and playful delivery may make it affectionate. Acoustic analysis helps, but only dialogue context can decide what the utterance means here.
Recommended architecture for call analytics
- 01
Transcribe with timestamps. Keep speaker turns, timing, and confidence so later signals can be aligned to the actual interaction.
- 02
Score short acoustic spans. Measure emotion and speaking style locally rather than averaging an entire call into one number.
- 03
Retain the separate channels. Store words, acoustic scores, and metadata independently. Do not call one field “sentiment” and discard provenance.
- 04
Apply domain context. Combine the channels with conversation stage, customer history, product events, and a domain-specific policy.
- 05
Escalate patterns, not people. Use scores to find moments for review or aggregate trends; do not infer protected traits, mental state, or truthfulness.
One API response, separate evidence
The unified oruk analysis endpoint returns transcript text, word timing, emotion scores, speaking-style scores, and time-local segments. Your application keeps those fields separate and makes the contextual decision.