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AI Powered Call Quality Monitoring

Overview​

This solution document details the architecture and technical implementation of an AI-powered call quality monitoring system. The system provides end-to-end capabilities to process call recordings from existing contact centers, delivering actionable insights such as sentiment analysis, call transcription, categorization, and post-call analytics.

High-Level Architecture Diagram​

post-call-analytics-high-level-architecture-diagram

GitHub - aws-samples/amazon-transcribe-post-call-analytics

Components​

post-call-analytics-components-diagram

https://drive.google.com/file/d/1m1S1eTfrySq2AauD4gNb5eByDW9pyfxU/view?usp=drive_link

  1. Source Input Data
    1. Audio Files: Delivered to an ingestion location in Amazon S3.
    2. Transcript Files: Generated by Amazon Transcribe and stored in S3.
  2. Transcription
    1. Batch processing of audio files using Amazon Transcribe.
    2. Features include:
      1. Custom vocabulary for domain-specific terminology.
      2. PII redaction and vocabulary filtering.
      3. Multi-language support with automatic detection.
      4. Caller and agent speaker labels using speaker diarization or channel identification.
  3. Data Processing & Enrichment
    1. Sentiment Analysis: Detects caller and agent sentiment trends.
    2. Talk & Non-Talk Time: Measures speaking and silence intervals.
    3. Interruption Detection: Identifies overlapping speech.
    4. Entity Detection: Uses Amazon Comprehend for extracting entities.
    5. Loudness Analysis: Normalized loudness metrics for both parties.
  4. Analytics Engine
    1. Provides insights such as:
      1. Sentiment trends.
      2. Call categorization based on keywords, sentiment, and interruptions.
      3. Issue detection using pre-built ML models.
    2. Generates summaries for key call information.
  5. Search Index
    1. Indexes call attributes such as time range, sentiment, entities, and transcription for efficient search capabilities.
  6. Dashboards & Reporting
    1. Visualizations include:
    2. Call trends (sentiment, loudness, interruptions).
    3. Training insights for quality assurance.
    4. Adherence to compliance standards.

Technical Details​

Data Flow​

  1. Ingestion
    1. Audio and transcript files are uploaded to Amazon S3 buckets.
    2. Notifications (using S3 events) trigger processing workflows via AWS Lambda or Step Functions.
  2. Transcription
    1. Audio files are processed using Amazon Transcribe.
    2. Transcriptions are stored in S3 for further processing.
  3. Processing Pipeline
    1. AWS Glue processes transcripts and audio metadata.
    2. Sentiment analysis and entity detection are performed using Amazon Comprehend.
    3. Custom categorization is applied based on business rules.
  4. Storage
    1. Processed data is stored in Amazon RDS or DynamoDB for structured queries.
    2. Elasticsearch or OpenSearch is used for indexing and search.
  5. Analytics & Reporting
    1. Amazon QuickSight provides dashboards for real-time monitoring.
    2. Reports can be exported to PDF or CSV formats.

Key AWS Services​

  • Amazon S3: For audio and transcript storage.
  • Amazon Transcribe: For audio-to-text conversion.
  • Amazon Comprehend: For NLP tasks such as sentiment and entity analysis.
  • Amazon RDS/DynamoDB: For structured data storage.
  • Amazon OpenSearch: For search indexing and querying.
  • AWS Glue: For ETL processes.
  • Amazon QuickSight: For analytics and reporting.
  • AWS Lambda: For event-driven processing.

Features​

Call Characteristics​

  • Interruption Detection: Identifies interruptions during calls.
  • Talk Time & Speed: Measures speech duration and words per minute.
  • Loudness Analysis: Detects yelling or speaking softly.
  • Non-Talk Time: Tracks periods of silence.

Generative Summarization​

  • Automatically summarizes calls, highlighting key issues, resolutions, and next steps.

Toxic Speech Detection​

  • Flags abusive or harmful speech using pitch, tone, and content analysis.

Scalability & Performance​

  • Auto-scaling: Leverages AWS Lambda and Step Functions for variable call volumes.
  • Data Partitioning: Ensures performance across large datasets using DynamoDB partitioning.
  • Monitoring: Uses AWS CloudWatch for real-time system monitoring.

Security​

  • PII Redaction: Ensures sensitive information is removed from transcripts and audio.
  • Encryption: S3 buckets and databases use server-side encryption.
  • Access Control: Managed via IAM roles and policies.

Conclusion​

This AI-powered call quality monitoring system provides a robust, scalable, and secure solution for deriving actionable insights from call data. Leveraging AWS services, it ensures efficient processing, accurate analytics, and seamless integration with existing contact center workflows.