Accent Detection & TTS Drift Monitoring

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Overview

Accent Detection identifies which English accent each participant speaks with across every segment of a call. This is useful for:

• TTS consistency monitoring — Verify your agent’s text-to-speech voice maintains the expected accent throughout the call
• Accent drift detection — Flag calls where the agent’s accent shifted mid-conversation
• Regional analysis — Understand the accent distribution of your callers

The model classifies audio into 16 English accent variants: American, British, Australian, Canadian, Indian, Irish, Scottish, Welsh, African, New Zealand, Hong Kong, Malaysian, Philippine, Singaporean, Bermudian, and South Atlantic.

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Prerequisites

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1. Enable the Accent Detection Package

Navigate to Settings > Analysis Packages and enable Accent Detection.

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2. Understand the Metrics

The package includes two metrics:

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Recipe: Detect Agent TTS Accent Drift

This recipe sets up automatic monitoring to flag any call where your agent’s TTS accent drifts from its expected voice.

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Step 1: Create a Metric Policy

1. Go to Metrics > Policies
2. Create a new policy or edit an existing one
3. Add the Accent Stability metric from the Accent Detection package
4. Configure a threshold:
   • Operator: >=
   • Value: 0.7
   • Participant Role: Agent

This evaluates every call and flags any where the agent’s accent stability drops below 70% — meaning the detected accent changed for more than 30% of the agent’s speaking time.

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Step 2: Review Results on the Call Detail Page

When a call is processed, open it and check the Metrics tab: Call-level accent card — Shows the dominant accent per participant with a probability distribution. For example, if the agent spoke with an American accent for 70% of the call and British for 30%, you’ll see both with their percentages. Segment-level probability chart — Shows a stacked area chart of accent probabilities over time. This lets you see exactly where in the call the accent shifted. Use the participant filter to focus on the Agent.

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Step 3: Set Up Alerts

Once you’ve configured the threshold:

• Calls that pass — The agent maintained a consistent accent throughout
• Calls that fail — The agent’s accent drifted beyond your tolerance, appearing as a failed threshold on the Overview tab

You can use webhooks to get notified when metric collection completes, then check the threshold results programmatically.

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How Accent Scores Work

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Per-Segment Scores

Each segment shows the accent probabilities after normalization. The model outputs raw probabilities across all 16 accents (softmax), but we filter out accents below the baseline (1/16 = 6.25%) and renormalize so the remaining scores sum to 100%. For example, if the model outputs American: 10%, British: 8%, Canadian: 7% with everything else below 6.25%, the normalized scores become American: 40%, British: 32%, Canadian: 28%.

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Call-Level Scores

Call-level accent scores represent the proportion of speaking time classified as each accent. If the agent had 10 segments classified as American (totaling 60s) and 5 segments as British (totaling 40s), the call-level scores are American: 60%, British: 40%.

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Accent Stability

Accent Stability is the proportion of speaking time the dominant accent held. In the example above, stability would be 0.6 (60%). A stability of 1.0 means every segment was classified as the same accent.

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Limitations

• English only — The current model classifies English accents only. Future language models will use the same infrastructure.
• Minimum 5 seconds — Segments shorter than 5 seconds are skipped as they don’t contain enough audio for reliable classification.
• Similar accents — The model may confuse similar accents (e.g. American vs Canadian, British vs Irish) especially on short segments. The normalization helps but isn’t perfect.