VoiceOps-fying �Low-Latency Intelligence Extraction from Messy Audio Streams

Dippu Kumar Singh

Leader of Emerging Technologies

Fujitsu North America Inc.

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TABLE OF CONTENTS

01

02

03

04

Current Challenges

Solution Provided

Key Outcomes

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Roadmap Ahead

Contact Center Operational Challenges, Call Time vs After-call Work Burden

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High Level Solution Architecture, Key Components, Summarization Workflow Logic

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Key Impacts and ROI Outcomes

Key Constraints and Continuous Improvement Plan

Current Challenges

Contact Center Operational Challenges

Over 50% of contact centers identify hiring and productivity as critical barriers to success.

Call Time vs After-Call Work (ACW)

• Average call time: 6.6 minutes vs Average post-processing time: 6.3 minutes, nearly a 1:1 ratio.
• Summarization quality varies by operator skill, creating inconsistency.
• Targeting ACW with AI can reduce post-processing time by an estimated 50%.

6.6

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Avg. Call Time

in minutes

6.3

Avg. ACW Time

in minutes

79.2%

Centers Expecting AI Gains

Core Mission

Shift focus from "handling calls" to "analyzing VOC" (Voice of Customer) for business growth.

Solution Provided

Solution Component Architecture

Voice

Capture

Capturing raw high-fidelity audio data.

Speech-To-Text (STT) Engine

Converting speech to accurate text.

Generative AI

Core

Summarization and context reasoning.

Customer Data

Sync

Automated entry

and VOC reporting.

System Goal: Transform raw conversational audio into structured business intelligence with minimal human intervention.

Component #1 - Voice Capture

Audio Intake

Standardizing audio levels and removing back-office chatter.

Channel Mapping

Separating Agent (L) and Customer (R) for contextual clarity.

Security Layer

Secure streaming with early-stage sensitive data protection.

The entry point for clean, multi-channel data acquisition.

Component #2 – STT Engine

Converting speech phonemes to high-accuracy digital text.

Acoustic Modeling

Interpreting raw sound into linguistic units across dialects.

Language Logic

Applying language specific dictionaries for accuracy.

Post-Processing

Converting "five thousand dollars" to “$5,000" for readability.

Component #3 – Generative AI Core�

LLM-driven reasoning for intent, sentiment, and summary

Orchestration

Guiding the LLM with specific task templates and samples.

Reasoning

Determining the "why" behind the call and customer emotion.

Trust Layer

Ensuring the summary is factually grounded in the transcript.

Hallucination Check

Token Optimizer

Intent Extraction

Sentiment Score

Prompt Engine

Few-shot Library

Component #4 – Customer Data Sync�

Translating AI insights into enterprise system actions.

API Gateway

Mapping AI fields (e.g., "Inquiry") to CRM database fields.

Verification

Allowing operators to review and approve the auto-summary.

Business Intelligence

Feeding categorized data into executive dashboards & FAQs.

Field Validation

Agent Confirmation

VOC Aggregator

FAQ Generator

Schema Mapper

REST Bridge

Summarization Workflow Logic

Raw

Transcript

Speaker

Separation

Context

Deduction

Structured

Output

Time-indexing

Confidence Scoring

Denoising

Channel Splitting

Voiceprints

Dialogue Stitching

Intent Recognition

Entity Spotting

Sentiment Analysis

Bullet Points

JSON Schema

Template Matching

Key Outcomes

Key Outcomes

Roadmap Ahead

Key Constraints

STT Accuracy

Summarization quality is directly tied to the accuracy of the initial STT conversion. Engines with >90% accuracy are recommended.

Initial Setup Cost

Initial consumption of API tokens and its associated consumption costs are on a higher side during early adoption phases.

Security & Compliance

Handling PII (Personally Identifiable Information) requires robust masking and secure cloud environments.

Upcoming Roadmap

Thank You

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