Intelligent Voice Assistant for Real-Time Call Document Retrieval
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Solution Overview
Problem
Customer service representatives face challenges in manually searching for answers during calls, leading to increased call times and resource strain, despite efforts to accelerate representative onboarding.
Innovation Solution
A system that converts speech to text and phonemes in real-time, identifies probable business categories, and detects similar document sections to provide relevant information to representatives during calls, using transformer models and Siamese LSTM networks for real-time document retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer representatives manually search for answers during calls, then they can provide accurate information, but call time increases and productivity decreases
Solution Approach 1:
The system enables self-service by automatically analyzing call content and retrieving relevant documents without human intervention. The speech-to-text conversion, phoneme sequence generation, business category identification, and document retrieval all occur automatically, allowing representatives to focus on customer interaction rather than information search
Solution Approach 2:
The system performs preliminary actions by pre-processing speech into text, converting text to phoneme sequences, and pre-identifying business categories before the representative needs the information. Documents are retrieved and ranked in advance based on the analyzed call content, so they are ready for immediate presentation
2Productivity
If more customer representatives are onboarded to handle increased call volume, then customer service capacity increases, but training time and resource strain increase
Solution Approach 1:
The system reduces dependency on representative expertise by automatically performing information retrieval and document ranking. New representatives can handle calls effectively without extensive training because the system autonomously analyzes call content and provides relevant documents, eliminating the need for deep product knowledge or search skills
Solution Approach 2:
The system replaces the mechanical process of human information search and knowledge application with automated computational processes. Speech analysis, phoneme conversion, category identification, and document retrieval are all performed by machine learning models rather than human representatives, reducing the skill barrier for new hires
3Productivity
If real-time speech-to-text conversion and document retrieval is implemented, then call time decreases and productivity increases, but system complexity increases
Solution Approach 1:
The system segments the complex task of information retrieval into distinct modular components: speech-to-text conversion module, phoneme sequence generation module, business category identification module, document similarity detection module, and document ranking module. Each module handles a specific sub-task independently, making the overall system more manageable and maintainable despite its complexity
Solution Approach 2:
The system introduces intermediary components to bridge different processing stages: phoneme sequences serve as intermediaries between speech/text and business category identification, while business categories act as intermediaries between raw speech content and final document retrieval. These intermediaries structure the data flow and enable modular processing
Data Source
AI summary
A computer-implemented method is provided for recommending at least one pertinent electronic document for supporting a call between a customer and an agent. The method includes converting in real time content of the call between the customer and the agent from speech to digitized text, isolating a predefined number of words in the digitized text of the converted call content as the call is in progress and converting the predefined number of words in text to a phoneme sequence. The method also includes identifying at least one probable business category associated with the phoneme sequence and detecting sections of one or more documents associated with the probable business category that are similar to the content of the call.