Voice text analytics

This Speech-to-Text web technology automatically converts full audio recordings to text to quickly identify the root cause of customer interaction problems. The speech analytics uses sentiment analysis algorithms and trend analysis algorithms to identify deviation in customer care executive's behavior. It also checks for compliance during the discussion and analyzes differences in pitch and tone to identify customers' emotions. This helps clients to identify difficult conversations over calls and flags them instantly. Additionally, suppose multiple speakers are on the call. In that case, the system is designed to identify various speakers and categorize the conversation for text analysis to ensure accuracy.

Key Features
  • Full audio recording to text transcription
  • Multi-party speaker support and auto-identification of voice for transcription tagging
  • Sentiment analysis of speakers based on the difference in pitch and tone
  • Question: Can this be real-time call sentiment monitoring and scoring solution?
  • Deviation analysis for customer care executive
Technologies Used
  • Python, Lambda, Comprehend (NLP), QuickSight, MySQL, HTML, J
  • Cloud: AWS
  • Compliance and regulatory verification for the discussion text
  • Auto-flagging of problematic call for further analysis
  • Useful for real-time call sentiment monitoring and scoring, performance management, and service level management of call center
  • Useful as a tool for internal training of staff to handle multiple scenarios and customer sentiments on the call

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