Audio Handler Filtering Extraneous Caller Speech in Voicebot Dialog
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Solution Overview
Problem
Conventional voicebots struggle with conversational and real-world caller dialog due to confusion from extraneous audio, out-of-sequence information, and subjective performance evaluation, leading to inefficiencies and poor user experience.
Innovation Solution
An intelligent voice interface system that includes an audio handler to filter irrelevant audio, handles out-of-sequence dialog, infers user states, and uses NLP models to accurately interpret caller intents, combined with a call review tool for manual performance analysis and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional voicebots use strict menu-driven IVR systems, then the structure is simple and easy to control, but the system cannot handle conversational or real-world caller dialog
Solution Approach 1:
The voicebot system is divided into separate functional modules: an audio handler for processing audio signals, an NLP model for language understanding, and a dialog manager for conversation control. This segmentation allows each component to specialize in handling specific aspects of conversational interaction, improving overall adaptability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
An audio handler is introduced as an intermediary component between the caller's voice input and the NLP model. This audio handler processes audio signals, filters out extraneous sounds, and prepares clean audio data for NLP processing, enabling the system to handle real-world conversational scenarios while maintaining structured processing through a clear intermediary layer.
2Reliability
If conventional voicebots use basic NLP processing, then the system is simpler to implement, but it becomes confused by extraneous audio, pauses, and side conversations
Solution Approach 1:
The audio handler performs preliminary processing of audio signals before they reach the NLP model. It filters out extraneous audio, identifies relevant speech segments, and prepares cleaned audio data in advance. This preliminary action ensures that the NLP model receives high-quality input, improving intent recognition accuracy while containing audio processing complexity within a dedicated preliminary processing layer.
Solution Approach 2:
The audio handler extracts and isolates relevant caller speech from extraneous audio elements such as background noise, pauses, and side conversations. By taking out only the useful information and discarding irrelevant audio, the system improves NLP processing accuracy without requiring the entire audio processing system to handle all complexity simultaneously.
3Measurement precision
If manual review of calls is performed to evaluate voicebot performance, then detailed analysis is possible, but the evaluation process is time consuming and subjective
Solution Approach 1:
The system automatically generates feedback reports that include performance metrics, call summaries, and identification of problematic interactions. This automated feedback provides precise measurement data without requiring manual review, significantly reducing evaluation time while maintaining high precision through structured metrics and objective data analysis.
Solution Approach 2:
The voicebot system performs self-evaluation by automatically analyzing its own performance through integrated monitoring and feedback mechanisms. The system identifies its own strengths and areas for improvement without external human intervention, enabling precise performance measurement while eliminating the time-consuming manual review process.
4Adaptability or versatility
If voicebots require highly ordered sequence of inputs, then the dialog flow is controlled and predictable, but it cannot handle out-of-sequence information or imprecise identifications
Solution Approach 1:
The dialog manager implements dynamic adaptation to handle out-of-sequence and imprecise caller inputs. Rather than following a rigid predetermined sequence, the system adjusts its response based on the actual caller input, maintaining flexibility and adaptability while managing complexity through dynamic decision-making algorithms that respond to real-time conversation context.
Data Source
AI summary
A method provides identification of relevant caller dialog with an intelligent voice interface configured to lead callers through pathways of an algorithmic dialog including available voice prompts. The method may include, during a voice communication with a caller, receiving from the caller device caller input data indicative of a voice input of the caller, and determining, by processing the caller input data, that a first portion of the voice input is intended to convey caller information to the intelligent voice interface, and that a second portion of the voice input is not intended to convey caller information. The method may also include identifying relevant caller information by analyzing the first portion of the voice input without the second portion of the voice input, and storing the relevant caller information in a database and/or selecting a pathway through the algorithmic dialog based upon the relevant caller information.