Accent Translation System Using Neural Network Models
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Solution Overview
Problem
Individuals with different accents often face difficulties in understanding each other, even when speaking the same language, due to variations in audio characteristics such as pitch, tone, and stress, which existing technologies have not effectively addressed.
Innovation Solution
An accent translation system that collects audio samples, classifies them into accent sample sets, generates translation models by comparing audio characteristics, and adjusts these characteristics to translate speech from one accent to another, including the use of neutral or combined accents, employing machine learning and artificial neural networks for refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio characteristics are adjusted to translate between accents, then communication understanding is improved, but system complexity increases
Solution Approach 1:
The patent introduces an accent translation system that acts as an intermediary between speakers with different accents. The system collects audio samples from multiple speakers, identifies accent characteristics, and generates translation models that convert audio characteristics between different accents. This intermediary system resolves the communication barrier without requiring direct adaptation between speaker pairs, thereby improving understanding while managing complexity through automated processing.
Solution Approach 2:
The patent applies parameter changes by modifying audio characteristics such as pitch, tone, stress, and rhythm to translate between different accents. The system analyzes audio samples to identify accent-specific parameters and then adjusts these parameters in real-time during audio communication. This approach enables accent translation by systematically changing acoustic parameters rather than requiring complex structural modifications to the communication system.
2Measurement precision
If accent translation models are generated by comparing audio samples, then translation accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-collecting and analyzing audio samples from multiple speakers before actual translation is needed. The system gathers accent samples, identifies characteristic patterns, and generates translation models in advance. This preparatory work creates ready-to-use translation models that can be applied quickly during real-time communication, thereby achieving high translation accuracy without excessive processing time during actual use.
Solution Approach 2:
The patent uses copying by creating translation models that replicate accent characteristics based on analyzed audio samples. Instead of analyzing and transforming audio in real-time, the system creates simplified representations (copies) of accent patterns that can be quickly applied during translation. This approach maintains translation accuracy by preserving essential accent features while reducing processing time through the use of pre-generated model copies.
3Adaptability or versatility
If multiple accent sample sets are collected and analyzed, then translation versatility is improved, but data processing complexity increases
Solution Approach 1:
The patent applies universality by designing a translation system that can handle multiple accent pairs through a single unified framework. The system collects audio samples from diverse speakers, identifies common accent characteristics, and generates translation models that can be applied across different accent combinations. This multi-functional approach enables the system to translate between various accents without requiring separate specialized systems for each accent pair, thereby improving versatility while managing processing complexity through standardized methods.
Data Source
AI summary
Techniques for accent translation are described herein. A plurality of audio samples may be received, and each of the plurality of audio samples may be associated with at least one of a plurality of accents. Audio samples associated with at least a first accent of the plurality of accents may be compared to audio samples associated with at least one other accent of the plurality of accents. A translation model between the first accent and a second accent may be generated. An input audio portion in a first spoken language may be received. It may be determined whether the input audio portion is substantially associated with the first accent, and if so, an output audio portion substantially associated with the second accent in the first spoken language may be outputted based, at least in part, on the translation model.