Automated Voice Translation via Feature Vector Encoding
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Solution Overview
Problem
Current voice translation technologies face challenges in accurately translating voice inputs due to differences in language expressions and sentence structures, leading to variations in recognition and translation outcomes, even for similar or identical meanings.
Innovation Solution
An automated interpretation method that encodes a voice signal in a first language to generate a feature vector, decodes it to produce a sentence in the first language, encodes this sentence to generate a feature vector for a second language, decodes it to produce a sentence in the second language, and selects a final translation from a candidate list based on various feature vectors and sentences, enhancing translation accuracy by considering multiple candidate sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct translation method is used, then translation speed is improved, but translation accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate translations before selecting the final translation. The translation engine produces several candidate sentences, which are then evaluated using similarity calculations against the original voice signal and intermediate representations. This preliminary generation of multiple options allows the system to maintain high speed while ensuring accuracy through subsequent selection based on similarity metrics.
Solution Approach 2:
The system implements feedback mechanisms by calculating similarity between the original voice signal, intermediate voice/sentence representations, and candidate translations. The similarity calculation results feed back into the selection process, allowing the system to iteratively evaluate and choose the most accurate translation from multiple candidates, thereby resolving the contradiction between speed and accuracy.
2Measurement precision
If multiple candidate sentences are considered, then translation accuracy is improved, but processing complexity increases
Solution Approach 1:
The system introduces intermediary representations (encoded voice features and decoded sentence features) as mediators between the original voice signal and candidate translations. These intermediaries serve as reference points for similarity calculation, enabling the system to evaluate multiple candidates efficiently without excessive complexity. The intermediaries act as a bridge that simplifies the comparison process.
Solution Approach 2:
The system changes parameters by transforming the voice signal into different feature space representations (encoded voice features, decoded sentence features) and evaluating candidate translations based on similarity in this transformed space. This parameter transformation allows for efficient comparison and selection among multiple candidates while managing processing complexity through mathematical operations in feature space.
3Reliability
If voice signal encoding and decoding is performed multiple times, then translation robustness is improved, but computational cost increases
Solution Approach 1:
The system performs preliminary encoding of the voice signal into feature representations before the translation process. These pre-encoded features are then reused multiple times during candidate evaluation and similarity calculations, avoiding the need to re-encode the voice signal from scratch. This preliminary action reduces computational cost while maintaining robustness through multiple uses of the encoded features.
Solution Approach 2:
The system creates copies of the encoded voice features and decoded sentence features, which are then used as references for evaluating multiple candidate translations. Instead of re-processing the original voice signal multiple times, the system copies and reuses the intermediate feature representations, significantly reducing computational cost while maintaining translation robustness through multiple evaluation points.
Data Source
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AI summary
Provided is an automated interpretation method, apparatus, and system. The automated interpretation method includes encoding a voice signal in a first language to generate a first feature vector, decoding the first feature vector to generate a first language sentence in the first language, encoding the first language sentence to generate a second feature vector with respect to a second language, decoding the second feature vector to generate a second language sentence in the second language, controlling a generating of a candidate sentence list based on any one or any combination of the first feature vector, the first language sentence, the second feature vector, and the second language sentence, and selecting, from the candidate sentence list, a final second language sentence as a translation of the voice signal.