Processing Genesys Speech API ASR Results with Node.js

What You Will Build

A Node.js service that retrieves Automatic Speech Recognition results from Genesys Cloud, validates them against age limits, language codes, and word error rate thresholds, constructs process payloads with result ID references and confidence directives, submits them via the Speech API, triggers external NLP webhooks, and maintains structured audit logs with latency tracking. This tutorial uses the Genesys Cloud Speech API and OAuth 2.0 Client Credentials flow. The implementation is written in modern Node.js using axios and the @genesyscloud/node-sdk package.

Prerequisites

• Genesys Cloud OAuth 2.0 Client Credentials grant with scopes: speech:results:read, speech:results:write, speech:process:write
• Node.js 18 LTS or higher
• @genesyscloud/node-sdk v5.0+
• axios v1.6+
• dotenv v16+ for environment variable management
• Access to a Genesys Cloud environment with Speech Analytics enabled

Authentication Setup

Genesys Cloud requires OAuth 2.0 Bearer tokens for all API calls. The following implementation fetches a token, caches it with a time-to-live check, and handles refresh logic automatically. The token endpoint requires the speech:results:read and speech:process:write scopes.

import axios from 'axios';
import dotenv from 'dotenv';

dotenv.config();

const OAUTH_URL = 'https://api.mypurecloud.com/oauth/token';
const GRANT_TYPE = 'client_credentials';
const SCOPES = 'speech:results:read speech:results:write speech:process:write';

let cachedToken = null;
let tokenExpiry = 0;

async function getAccessToken() {
  if (cachedToken && Date.now() < tokenExpiry) {
    return cachedToken;
  }

  const response = await axios.post(OAUTH_URL, {
    grant_type: GRANT_TYPE,
    client_id: process.env.GENESYS_CLIENT_ID,
    client_secret: process.env.GENESYS_CLIENT_SECRET,
    scope: SCOPES
  }, {
    headers: { 'Content-Type': 'application/x-www-form-urlencoded' }
  });

  cachedToken = response.data.access_token;
  tokenExpiry = Date.now() + (response.data.expires_in * 1000) - 5000;
  return cachedToken;
}

export { getAccessToken };

The token cache prevents redundant authentication requests. The five-second buffer accounts for network latency and ensures the token remains valid during long-running batch operations.

Implementation

Step 1: Fetch and Validate Raw ASR Results

The first operation retrieves pending speech results using the GET /api/v2/speech/results endpoint. This endpoint supports pagination via the pageSize and pageNumber parameters. The response contains transcript matrices, confidence scores, and metadata required for downstream validation.

import { getAccessToken } from './auth.js';
import axios from 'axios';

const BASE_URL = 'https://api.mypurecloud.com';
const MAX_RESULT_AGE_MS = 24 * 60 * 60 * 1000; // 24 hours
const ALLOWED_LANGUAGES = ['en-US', 'en-GB', 'es-ES'];

async function fetchAndValidateResults(pageSize = 50, pageNumber = 1) {
  const token = await getAccessToken();
  
  try {
    const response = await axios.get(`${BASE_URL}/api/v2/speech/results`, {
      headers: { Authorization: `Bearer ${token}` },
      params: { pageSize, pageNumber }
    });

    const results = response.data.entities || [];
    const validResults = [];

    for (const result of results) {
      const createdMs = new Date(result.dateCreated).getTime();
      const ageMs = Date.now() - createdMs;

      if (ageMs > MAX_RESULT_AGE_MS) {
        console.warn(`Skipping result ${result.id}: exceeds maximum age limit`);
        continue;
      }

      if (!ALLOWED_LANGUAGES.includes(result.languageCode)) {
        console.warn(`Skipping result ${result.id}: unsupported language ${result.languageCode}`);
        continue;
      }

      validResults.push(result);
    }

    return {
      validResults,
      hasNextPage: response.data.nextPage != null
    };
  } catch (error) {
    if (error.response?.status === 401) {
      throw new Error('Authentication failed. Token is invalid or expired.');
    }
    if (error.response?.status === 403) {
      throw new Error('Forbidden. Check OAuth scopes and resource permissions.');
    }
    if (error.response?.status === 429) {
      const retryAfter = error.response.headers['retry-after'] || 5;
      console.warn(`Rate limited. Retrying in ${retryAfter} seconds.`);
      await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
      return fetchAndValidateResults(pageSize, pageNumber);
    }
    throw error;
  }
}

export { fetchAndValidateResults };

The validation pipeline enforces two critical constraints: maximum result age and language code verification. Genesys Speech engines degrade in accuracy when processing stale audio artifacts. The language code check ensures downstream NLP consumers receive transcripts in supported locales. The retry logic handles 429 responses by reading the Retry-After header and resubmitting the request.

Step 2: Construct Process Payloads and Enforce Engine Constraints

Before submission, the service must construct a process payload containing result ID references, a transcript matrix, and a confidence directive. The Speech API rejects payloads that violate engine constraints or contain malformed confidence directives.

function calculateWordErrorRate(hypothesis, reference) {
  const hypoWords = hypothesis.toLowerCase().split(/\s+/);
  const refWords = reference.toLowerCase().split(/\s+/);
  const total = Math.max(hypoWords.length, refWords.length);
  
  let matches = 0;
  for (let i = 0; i < Math.min(hypoWords.length, refWords.length); i++) {
    if (hypoWords[i] === refWords[i]) matches++;
  }
  
  return total === 0 ? 0 : 1 - (matches / total);
}

function buildProcessPayload(validResults, confidenceThreshold = 0.75) {
  const resultIds = [];
  const transcriptMatrix = {};
  let confidenceDirective = 'ACCEPT_HIGH_CONFIDENCE';

  for (const result of validResults) {
    const avgConfidence = result.transcripts.reduce((sum, t) => sum + t.confidence, 0) / result.transcripts.length;
    
    if (avgConfidence < confidenceThreshold) {
      confidenceDirective = 'REJECT_LOW_CONFIDENCE';
      continue;
    }

    if (result.referenceTranscript) {
      const wer = calculateWordErrorRate(result.transcripts[0].text, result.referenceTranscript);
      if (wer > 0.20) {
        console.warn(`Result ${result.id} exceeds WER threshold. Excluding from process batch.`);
        continue;
      }
    }

    resultIds.push(result.id);
    transcriptMatrix[result.id] = {
      text: result.transcripts[0].text,
      confidence: avgConfidence,
      languageCode: result.languageCode,
      channels: result.transcripts.map(t => t.channel)
    };
  }

  if (resultIds.length === 0) {
    throw new Error('No valid results meet processing constraints. Aborting payload construction.');
  }

  return {
    resultIds,
    transcriptMatrix,
    confidenceDirective,
    engineConstraints: {
      maxBatchSize: 50,
      requiredFormat: 'json',
      processingMode: 'batch'
    }
  };
}

export { buildProcessPayload };

The transcriptMatrix maps each result ID to its primary transcript, confidence score, and channel routing data. The confidenceDirective instructs the Speech engine on how to handle borderline confidence scores. The word error rate calculation compares hypothesis output against reference transcripts when available. Results exceeding a 0.20 WER threshold are excluded to prevent downstream parsing errors.

Step 3: Submit Process Requests and Handle Atomic Consumption

The process submission uses POST /api/v2/speech/process. This operation is atomic. The API consumes the referenced result IDs upon successful submission, preventing duplicate processing. Format verification occurs server-side before queueing the batch.

import { getAccessToken } from './auth.js';
import axios from 'axios';

const BASE_URL = 'https://api.mypurecloud.com';

async function submitProcessPayload(payload) {
  const token = await getAccessToken();

  try {
    const response = await axios.post(`${BASE_URL}/api/v2/speech/process`, payload, {
      headers: {
        Authorization: `Bearer ${token}`,
        'Content-Type': 'application/json'
      }
    });

    console.log('Process submission successful:', response.data);
    return response.data;
  } catch (error) {
    if (error.response?.status === 400) {
      const details = error.response.data?.errors || [];
      throw new Error(`Schema validation failed: ${JSON.stringify(details)}`);
    }
    if (error.response?.status === 409) {
      throw new Error('Conflict. One or more result IDs have already been consumed.');
    }
    if (error.response?.status === 429) {
      const retryAfter = error.response.headers['retry-after'] || 5;
      await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
      return submitProcessPayload(payload);
    }
    throw error;
  }
}

export { submitProcessPayload };

The 400 response contains detailed schema violations. The 409 response indicates atomic consumption failure, meaning the result IDs were already processed by another consumer or a previous failed batch retained locks. The retry logic ensures transient rate limits do not halt the pipeline.

Step 4: Trigger Webhook Delivery and Synchronize with External NLP

Genesys Cloud automatically delivers result processed events to configured webhooks. This step demonstrates how to verify webhook delivery status and trigger external NLP consumers upon successful processing.

import axios from 'axios';

const WEBHOOK_URL = 'https://api.mypurecloud.com/api/v2/webhooks';
const EXTERNAL_NLP_ENDPOINT = process.env.EXTERNAL_NLP_URL;

async function verifyAndTriggerWebhook(processResultId, token) {
  try {
    const webhookResponse = await axios.get(`${WEBHOOK_URL}/${processResultId}/delivery`, {
      headers: { Authorization: `Bearer ${token}` }
    });

    const deliveryStatus = webhookResponse.data?.status || 'PENDING';
    
    if (deliveryStatus === 'DELIVERED') {
      await axios.post(EXTERNAL_NLP_ENDPOINT, {
        processResultId,
        timestamp: new Date().toISOString(),
        syncMode: 'ALIGNED',
        payload: {
          resultIds: webhookResponse.data.resultIds,
          processingStatus: 'COMPLETE'
        }
      }, {
        headers: { 'Content-Type': 'application/json' },
        timeout: 5000
      });
      
      console.log('NLP synchronization triggered successfully.');
    } else {
      console.warn(`Webhook delivery status: ${deliveryStatus}. Deferring NLP trigger.`);
    }
  } catch (error) {
    if (error.code === 'ECONNABORTED') {
      console.error('NLP consumer timeout. Event queued for retry.');
    } else {
      console.error('Webhook verification failed:', error.message);
    }
  }
}

export { verifyAndTriggerWebhook };

The webhook delivery endpoint confirms safe process iteration. The external NLP consumer receives a synchronized payload containing the process result ID and aligned timestamp. Timeout handling prevents blocking the main pipeline when downstream services experience latency.

Step 5: Implement Audit Logging and Latency Tracking

Speech governance requires deterministic audit trails. The following class tracks processing latency, consumption success rates, and writes structured logs for compliance.

class SpeechAuditLogger {
  constructor() {
    this.metrics = {
      totalProcessed: 0,
      successfulConsumptions: 0,
      failedConsumptions: 0,
      totalLatencyMs: 0,
      events: []
    };
  }

  logEvent(eventType, payload, latencyMs) {
    const entry = {
      timestamp: new Date().toISOString(),
      eventType,
      latencyMs,
      payload: JSON.parse(JSON.stringify(payload)),
      successRate: this.calculateSuccessRate()
    };
    
    this.metrics.events.push(entry);
    this.metrics.totalLatencyMs += latencyMs;
    console.log(JSON.stringify(entry));

    if (eventType === 'PROCESS_COMPLETE') {
      this.metrics.totalProcessed++;
      this.metrics.successfulConsumptions++;
    }
    if (eventType === 'PROCESS_FAILED') {
      this.metrics.totalProcessed++;
      this.metrics.failedConsumptions++;
    }
  }

  calculateSuccessRate() {
    if (this.metrics.totalProcessed === 0) return 0;
    return (this.metrics.successfulConsumptions / this.metrics.totalProcessed) * 100;
  }

  getMetrics() {
    return {
      ...this.metrics,
      averageLatencyMs: this.metrics.totalProcessed > 0 
        ? this.metrics.totalLatencyMs / this.metrics.totalProcessed 
        : 0
    };
  }
}

export { SpeechAuditLogger };

The logger maintains a running success rate and average latency. Each event includes a deep clone of the payload to prevent memory leaks from circular references. The structured JSON output integrates directly with centralized logging systems like Splunk or Datadog.

Complete Working Example

import { fetchAndValidateResults } from './fetch.js';
import { buildProcessPayload } from './payload.js';
import { submitProcessPayload } from './submit.js';
import { verifyAndTriggerWebhook } from './webhook.js';
import { SpeechAuditLogger } from './audit.js';
import { getAccessToken } from './auth.js';

async function runSpeechProcessor() {
  const logger = new SpeechAuditLogger();
  const startTime = Date.now();
  let pageNumber = 1;
  let hasMore = true;

  while (hasMore) {
    try {
      const { validResults, hasNextPage } = await fetchAndValidateResults(50, pageNumber);
      hasMore = hasNextPage;

      if (validResults.length === 0) {
        console.log('No valid results in current page. Advancing pagination.');
        pageNumber++;
        continue;
      }

      const payload = buildProcessPayload(validResults, 0.80);
      const submissionStart = Date.now();
      const processResult = await submitProcessPayload(payload);
      const latency = Date.now() - submissionStart;

      logger.logEvent('PROCESS_COMPLETE', processResult, latency);

      const token = await getAccessToken();
      await verifyAndTriggerWebhook(processResult.id, token);

      pageNumber++;
    } catch (error) {
      const latency = Date.now() - startTime;
      logger.logEvent('PROCESS_FAILED', { error: error.message }, latency);
      console.error('Batch processing failed:', error.message);
      break;
    }
  }

  console.log('Final Metrics:', logger.getMetrics());
}

runSpeechProcessor().catch(console.error);

This script orchestrates the full pipeline. It paginates through results, validates constraints, constructs payloads, submits them atomically, triggers webhooks, and logs every operation. The loop terminates when all pages are consumed or a fatal error occurs.

Common Errors & Debugging

Error: 400 Bad Request

• Cause: The process payload violates speech engine constraints. Common triggers include missing result IDs, malformed confidence directives, or transcript matrix schema mismatches.
• Fix: Validate the payload structure against the POST /api/v2/speech/process schema. Ensure confidenceDirective uses exact enum values like ACCEPT_HIGH_CONFIDENCE or REJECT_LOW_CONFIDENCE. Verify that transcriptMatrix keys match the resultIds array exactly.
• Code showing the fix:

  if (!['ACCEPT_HIGH_CONFIDENCE', 'REJECT_LOW_CONFIDENCE', 'PROCESS_ALL'].includes(confidenceDirective)) {
    throw new Error('Invalid confidence directive. Must match Speech API enum values.');
  }
  

Error: 401 Unauthorized

• Cause: The OAuth token is expired, revoked, or missing required scopes.
• Fix: Regenerate the token using the Client Credentials flow. Confirm the speech:results:read and speech:process:write scopes are attached to the OAuth client in the Genesys Cloud admin console.
• Code showing the fix:

  const token = await getAccessToken();
  if (!token) throw new Error('Token generation failed. Check client credentials.');
  

Error: 409 Conflict

• Cause: Atomic consumption failure. One or more result IDs were already processed by another consumer or a previous batch retained a processing lock.
• Fix: Implement idempotency checks before submission. Query the result status via GET /api/v2/speech/results/{speechResultId} to verify the processingStatus field is PENDING.
• Code showing the fix:

  const statusResponse = await axios.get(`${BASE_URL}/api/v2/speech/results/${resultId}`, {
    headers: { Authorization: `Bearer ${token}` }
  });
  if (statusResponse.data.processingStatus !== 'PENDING') {
    console.warn(`Result ${resultId} already processed. Excluding from batch.`);
    return;
  }
  

Error: 429 Too Many Requests

• Cause: Rate limit cascade. The Speech API enforces strict request quotas per tenant and per OAuth client.
• Fix: Implement exponential backoff with jitter. Read the Retry-After header and delay the next request. The provided implementation handles this automatically in the submission and fetch functions.
• Code showing the fix:

  const retryAfter = error.response.headers['retry-after'] || Math.min(2 ** attemptCount, 30);
  await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
  

Official References

• Genesys Cloud Speech API Documentation
• POST /api/v2/speech/process Endpoint Reference
• GET /api/v2/speech/results Endpoint Reference
• OAuth 2.0 Client Credentials Flow
• @genesyscloud/node-sdk Package Registry