Real-Time Dialect Modulation for Voice Clarity
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Solution Overview
Problem
Individuals with different accents and dialects often face difficulties in understanding each other during telephone calls, particularly in business communications and interactions with non-English native speakers or automated systems, due to the lack of visual cues and varying audio quality.
Innovation Solution
A method and apparatus for voice modification during calls that detects participants' dialects, selects a target dialect based on call characteristics, and modulates audio signals to enhance understanding by converting voices to conform to the target dialect, using a detection module, conversion module, and modulation module within an IP telephony system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice modification is applied to help participants with different accents understand each other, then communication clarity is improved, but device complexity increases due to dialect detection and audio modulation components
Solution Approach 1:
The patent introduces a server as an intermediary component that performs dialect detection and voice modulation. Instead of embedding complex processing capabilities in each client device, the server acts as a centralized mediator that receives audio streams, processes dialect information, and returns modified audio. This resolves the technical contradiction by externalizing the complexity from individual devices to a shared infrastructure, improving communication clarity while minimizing the complexity burden on end-user devices.
Solution Approach 2:
The server is designed to handle multiple functions: it serves as both a communication relay and a dialect processing engine. The same server infrastructure that routes calls also performs dialect detection and voice modulation. This multi-functionality approach reduces overall system complexity by consolidating multiple specialized components into a single universal platform, thereby improving communication clarity without proportionally increasing device complexity.
2Reliability
If real-time dialect detection and modulation is performed, then understanding between participants is enhanced, but processing time increases
Solution Approach 1:
The system performs preliminary dialect detection and analysis during the call setup phase or during periods when audio processing load is low. By detecting dialects in advance and preparing modulation parameters beforehand, the system minimizes real-time processing delays. This preliminary action ensures that when actual voice modulation is needed, the processing time is significantly reduced, thereby enhancing understanding accuracy without excessive processing time penalties.
Solution Approach 2:
The voice modulation process operates continuously throughout the call rather than being applied in discrete batches. The server maintains a continuous audio stream processing pipeline that detects and modulates dialects in real-time as audio flows through the system. This continuous processing eliminates interruptions and reduces overall processing time by avoiding repeated start-stop cycles, thereby enhancing understanding accuracy while minimizing time loss.
3Reliability
If audio signals are modulated to match target dialect, then communication effectiveness is improved, but loss of original voice characteristics occurs
Solution Approach 1:
The voice modulation system applies dialect-specific adjustments only to the portions of the audio signal that contain dialectal variations, rather than uniformly modifying the entire voice signal. Phoneme-level processing targets only the specific speech sounds that differ between dialects, leaving other voice characteristics such as pitch, tone, and timbre relatively preserved. This local quality approach improves communication effectiveness by addressing only the problematic dialectal elements while minimizing loss of original voice characteristics.
Solution Approach 2:
The system applies partial modulation rather than complete transformation of the voice signal. Instead of converting the entire voice to match the target dialect perfectly, it selectively modifies only the critical phonemes and pronunciation patterns that affect mutual understanding. This partial action approach maintains sufficient voice authenticity and characteristics while achieving the necessary communication effectiveness, thereby reducing information loss.
Data Source
AI summary
A method for voice modification during a telephone call comprising receiving a source audio signal associated with at least one participant, wherein the source audio signal comprises a voice of the at least one participant, detecting a source dialect of the at least one participant, selecting a target dialect based on at least a characteristic of a target participant and creating a modulated audio signal based on the source audio signal, the source dialect, and the target dialect and transmitting the modulated audio signal to the target participant.