Architecting Live Call Transcription Pipelines with Speaker Diarization for Agent Assist

How-To-Articles
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Architecting Live Call Transcription Pipelines with Speaker Diarization for Agent Assist

What This Guide Covers

This guide details the architectural implementation of real-time speech-to-text pipelines for inbound and outbound voice interactions in Genesys Cloud CX. You will configure the system to stream audio to the transcription service, enforce strict speaker diarization to distinguish between the agent and the customer, and route the resulting text stream to an external Agent Assist application via a WebSocket connection.

Prerequisites, Roles & Licensing

To implement live transcription with external routing, your organization requires specific licensing and permissions.

  • Licensing:

    • Genesys Cloud CX Voice: Standard or Premium license for agents and supervisors.
    • Speech Analytics Add-on: Required for the underlying transcription engine. Note that Live Transcription consumes separate storage and processing units compared to post-call analytics.
    • Agent Assist Add-on: Required if you intend to use the native Agent Assist features within Genesys Cloud. If you are building a custom external application, this is not strictly required, but you need access to the Developer Portal.

  • Permissions:

    • Administrator: System > Speech Analytics > Configure and System > Speech Analytics > Edit are required to enable the feature globally.
    • Architect: Architect > Flow > Edit is required to add the transcription nodes to your IVR flows.
    • API Client: The application receiving the WebSocket stream requires an OAuth 2.0 client with the speechanalytics:realtime scope.

  • External Dependencies:

    • Webhook Endpoint: A publicly accessible HTTPS endpoint (or a private endpoint via Genesys Cloud Private Connect) capable of accepting WebSocket connections.
    • Network Latency: The round-trip time between the Genesys Cloud edge and your application server should not exceed 150ms to ensure the “live” nature of the assist is perceptible to the agent. Higher latency results in text appearing on the agent’s screen significantly after the customer has spoken, breaking the cognitive flow.

The Implementation Deep-Dive

1. Global Speech Analytics Configuration

Before configuring any flow, you must enable Live Transcription at the organization level. This setting controls the engine availability and the default language models.

Navigate to Admin > Speech Analytics > Settings. Locate the Live Transcription section. Enable the toggle for Allow live transcription.

The Trap: Enabling Live Transcription globally does not automatically apply it to every call. It merely makes the capability available. If you fail to configure the Language and Model settings here, the system defaults to en-US with the Standard model. For high-noise environments or specialized domains (finance, healthcare), the Standard model yields poor accuracy. You must select the Enhanced model if your license permits, as it utilizes a larger vocabulary and context window.

Architectural Reasoning: Genesys Cloud uses a hybrid approach for transcription. The audio stream is processed in chunks (typically 10-20 seconds) to balance latency and accuracy. The “Live” designation means the system streams partial hypotheses (interim results) before finalizing the text. Your downstream application must handle these interim states gracefully. If your application treats every incoming packet as final truth, you will display flickering or incorrect text to the agent.

2. Flow Configuration for Inbound Calls

The transcription pipeline is initiated within the Genesys Cloud Architect flow. You cannot start transcription via API after the call begins; the intent must be declared before the audio bridge is established.

Step 2.1: The Begin Transcription Node

Drag a Begin Transcription node into your flow. Place it immediately after the Queue node (for inbound) or the Make Outbound Call node (for outbound).

Configure the node with the following parameters:

  • Transcription ID: Use a unique variable, such as transcriptionId. This ID links the live stream to the post-call analytics record.
  • Language: Set to en-US (or your target locale).
  • Diarization: Set to Enabled. This is critical. Without diarization, the output is a single stream of text with no speaker labels. With diarization, each word is tagged with a speaker field (0 for the first detected speaker, 1 for the second, etc.).

The Trap: Placing the Begin Transcription node before the Queue node. If you place it before the Queue, transcription begins during the IVR navigation or hold music. This generates massive amounts of irrelevant data (menu prompts, hold music artifacts) and consumes transcription units unnecessarily. More critically, the speaker diarization engine may lock onto the IVR voice as “Speaker 0,” causing the actual customer to be labeled as “Speaker 1” when they finally reach the agent. This breaks the logic in your Agent Assist application, which likely expects the customer to be the primary initiator. Always place the node after the agent has answered or immediately before the agent is connected.

Step 2.2: Routing the Transcript Stream

By default, Genesys Cloud stores the transcription for post-call analysis. To enable Agent Assist, you must route the live stream to an external application.

In the Begin Transcription node configuration, locate the Webhook section. Enable Stream to webhook.

  • URL: Enter the HTTPS URL of your Agent Assist service (e.g., wss://agent-assist.mycompany.com/transcribe).
  • Method: POST.
  • Headers: Include an Authorization: Bearer <token> header if your service requires authentication.

Architectural Reasoning: The webhook URL you provide must support WebSocket upgrades. Genesys Cloud initiates a WebSocket connection to this URL once the call is active. The payload sent over this socket is a JSON stream of transcription events. Your service must acknowledge the connection and remain open for the duration of the call. If the WebSocket drops, Genesys Cloud retries the connection, but there is a window of lost data. Your service should implement exponential backoff and reconnection logic.

3. Designing the Agent Assist Service

Your external service receives the live transcription stream. The responsibility of this service is to parse the diarized text, identify intent or entities, and push relevant information to the agent’s screen.

Step 3.1: Handling the WebSocket Payload

The payload structure from Genesys Cloud follows the W3C Web Speech API specification with extensions for diarization.

{
  "type": "transcript",
  "transcript": [
    {
      "start": 12345,
      "end": 14567,
      "speaker": 0,
      "text": "I would like to cancel my subscription",
      "confidence": 0.98
    }
  ],
  "isFinal": true
}

The Trap: Ignoring the isFinal flag. Genesys Cloud sends two types of updates:

  1. Interim (isFinal: false): The engine has a hypothesis of what was said, but it is not confident. The text may change in the next packet.
  2. Final (isFinal: true): The engine has finalized the text for this segment.

If your application displays interim text immediately, the agent will see words appear and then vanish or morph, which is distracting and unprofessional. Best practice is to buffer interim text in a “pending” state (perhaps displayed in a lighter gray font) and only commit to full opacity and trigger business logic when isFinal is true.

Step 3.2: Mapping Speakers to Roles

Diarization assigns numeric IDs (0, 1, 2) to speakers. It does not know who is the agent and who is the customer. You must map these IDs to roles.

In a standard inbound call:

  • The first speaker to talk after the agent answers is usually the customer.
  • The second speaker is the agent.

However, this is not guaranteed. The agent might say “Hello, thank you for calling,” before the customer speaks. In this case, the agent is Speaker 0 and the customer is Speaker 1.

Solution: Implement a role-mapping heuristic in your Agent Assist service.

  1. Wait for the first finalized utterance from the agent (identified by matching the agent’s name or ID from the call metadata).
  2. Assign that speaker ID to ROLE_AGENT.
  3. Assign the other active speaker ID to ROLE_CUSTOMER.
  4. Only trigger Agent Assist logic on utterances tagged with ROLE_CUSTOMER.

The Trap: Assuming Speaker 0 is always the customer. In outbound calls, the agent often speaks first. If your logic hardcodes Speaker 0 as the customer, you will trigger assist cards based on the agent’s own words, leading to nonsensical recommendations. Always dynamically resolve speaker roles.

4. Integrating with the Agent Desktop

The final step is delivering the assist content to the agent. This can be done via the Genesys Cloud Agent Assist widget or a custom Chrome extension/sidebar.

Option A: Native Agent Assist Widget

If you use the native widget, you must register an Agent Assist Application in Genesys Cloud.

  1. Go to Admin > Agent Assist > Applications.
  2. Create a new application.
  3. Set the Content URL to your web application.
  4. Enable Live Transcription.

When the call starts, Genesys Cloud opens an iframe to your Content URL and passes the transcriptionId in the URL parameters or via a postMessage event. Your application then subscribes to the WebSocket stream using the transcriptionId.

Option B: Custom Sidebar (Chrome Extension)

For more control, build a Chrome extension that injects a sidebar into the Genesys Cloud Agent Desktop.

  1. Listen for the genesys.ui.call.active event.
  2. Extract the transcriptionId from the call metadata.
  3. Establish a WebSocket connection to your backend service, passing the transcriptionId.
  4. Your backend service acts as a proxy, forwarding the Genesys stream to your extension via another WebSocket or Server-Sent Events (SSE).

Architectural Reasoning: Using a backend proxy is necessary because the Genesys Cloud WebSocket endpoint is not directly accessible from the browser due to CORS and authentication constraints. The backend service authenticates to Genesys Cloud using an OAuth token, opens the WebSocket, and then relays the data to the frontend. This also allows you to enrich the data (e.g., adding CRM context) before sending it to the agent.

Validation, Edge Cases & Troubleshooting

Edge Case 1: The “Silent Agent” Diarization Failure

The Failure Condition: The transcription stream shows only one speaker (Speaker 0). The agent speaks, but the text appears with the same speaker ID as the customer.
The Root Cause: The diarization engine relies on acoustic differences to distinguish speakers. If the agent uses a headset with poor microphone quality, or if the agent speaks very quietly, the engine may fail to detect a second speaker profile.
The Solution:

  1. Verify headset compatibility. Use headsets listed in the Genesys Cloud Compatibility Matrix.
  2. In the Begin Transcription node, ensure the Diarization setting is set to Enabled and not Disabled.
  3. If the issue persists, consider using Speaker Identification (if licensed) which uses voice print matching to explicitly tag the agent. This requires enrolling the agent’s voice print beforehand.

Edge Case 2: WebSocket Connection Drops During Long Calls

The Failure Condition: After 15-20 minutes, the Agent Assist interface stops updating. The transcription continues in Genesys Cloud (visible in post-call analytics), but the live stream to the application ceases.
The Root Cause: Intermediate network devices (load balancers, firewalls, or the Genesys Cloud edge itself) may drop idle WebSocket connections. If there is a pause in speech, the stream may be considered idle.
The Solution:

  1. Implement Ping/Pong mechanisms in your WebSocket server. Send a ping frame every 15-20 seconds.
  2. Configure your load balancer (e.g., AWS ELB, Nginx) to allow long-lived WebSocket connections (set proxy_read_timeout to 3600s or higher).
  3. In your Agent Assist application, implement automatic reconnection logic. If the WebSocket closes unexpectedly, attempt to reconnect using the same transcriptionId. Genesys Cloud allows reconnecting to an active transcription stream.

Edge Case 3: High Latency Causing “Ghost” Text

The Failure Condition: The agent hears the customer say “Yes,” but the text “No” appears on the screen for a second before correcting to “Yes.” This happens frequently.
The Root Cause: This is not a bug; it is the nature of streaming speech recognition. The engine makes a best guess based on the first few hundred milliseconds of audio. As more audio arrives, the guess changes. High network latency exacerbates this by delaying the arrival of corrective packets.
The Solution:

  1. UI Design: Display interim text with low opacity (e.g., 50% gray) and final text with high opacity (100% black). This visually signals to the agent that the text is not yet confirmed.
  2. Debounce Logic: Do not trigger business logic (e.g., searching the knowledge base) on every interim update. Wait for the isFinal flag or implement a debounce timer (e.g., wait 500ms after the last update before processing).
  3. Network Optimization: Ensure your Agent Assist service is hosted in a region geographically close to the Genesys Cloud edge processing the call. If your agents are in the US East, host your service in US East. Cross-region WebSocket connections add 50-100ms of latency, which is significant for real-time text.

Official References