Cross-Modal Emotion Analysis Engine

Emotion is the hidden variable in every customer conversation. A frustrated customer who types politely is about to escalate. A stressed caller whose voice wavers needs immediate attention. The Emotion Analysis Engine detects what neither text nor voice alone can reveal — by analyzing both channels simultaneously and correlating their signals over time.

1. The Problem: Single-Modal Blindness

Current emotion analysis tools suffer from a fundamental limitation: they analyze one modality at a time.

• Text-only tools (IBM Watson NLU, Google Cloud NL) detect sentiment from words but miss vocal cues — a customer saying “fine” in a strained voice reads as positive
• Voice-only tools (Cogito, Beyond Verbal) detect vocal patterns but miss textual context — a technical question asked calmly might have frustrated text history
• No tool currently fuses both modalities into a continuous session state with escalation tracking

The Emotion Analysis Engine is the first system to treat text and voice as parallel signal channels, correlate them in real time, and produce a unified conversation state.

2. Text Emotion Architecture

Scoring Model

Text emotion scoring uses a multi-feature approach rather than a single ML model, ensuring transparency and calibration control:

Output Contract

@dataclass(frozen=True)
class TextEmotionScores:
    frustration: float    # 0.0 - 1.0
    confusion: float      # 0.0 - 1.0
    urgency: float        # 0.0 - 1.0
    calm: float           # 0.0 - 1.0
    dominant_emotion: str  # highest scoring dimension
    confidence: float     # classification confidence

3. Voice Emotion Architecture

Acoustic Feature Extraction

Voice emotion analysis begins with extracting acoustic features from audio frames:

Voice Emotion Scores

@dataclass(frozen=True)
class VoiceEmotionScores:
    agitation: float       # 0.0 - 1.0
    stress: float          # 0.0 - 1.0
    calm: float            # 0.0 - 1.0
    hesitation: float      # 0.0 - 1.0
    dominant_emotion: str
    confidence: float

4. Emotion Tracking Over Time

Individual emotion scores are snapshots. The emotion tracker converts them into a continuous timeline:

Reason Codes

Every emotion event carries reason codes for auditability:

• TEXT_FRUSTRATION_SPIKE — frustration score jumped by > 0.2
• VOICE_HIGH_ENERGY — voice energy exceeded agitation threshold
• TEXT_NEUTRAL — text analysis shows no strong emotion
• VOICE_PITCH_RISE — pitch variance indicates increasing stress

5. Evidence Pack

The evidence pack bundles all session data for post-hoc review:

6. Implementation Metrics