Real-time Speech Translation via Discriminant Model
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Solution Overview
Problem
Current real-time speech translation systems experience significant delays due to low speech recognition frequency and the need for complete sentences with full stops, leading to poor translation effects.
Innovation Solution
A method and apparatus that utilize a pre-trained discriminant model to recognize and translate speech in real-time by concatenating recognized text and determining translation eligibility before full stops are reached, allowing for immediate translation without waiting for complete sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition waits for complete sentences with full stops before translation, then translation accuracy is improved, but translation delay increases
Solution Approach 1:
The discriminant model performs preliminary analysis on the recognized text to predict whether a full stop will appear soon. This allows the system to prepare for translation in advance and make informed decisions about when to translate, rather than passively waiting for full stops. The model evaluates features like text length, punctuation patterns, and semantic completeness to anticipate translation opportunities before they occur.
Solution Approach 2:
The system dynamically adjusts its translation triggering mechanism based on discriminant model predictions. Instead of using a static rule that only translates after full stops, the system adapts its behavior by translating earlier when the model predicts low probability of upcoming full stops, and waiting longer when full stops are likely. This dynamic adjustment resolves the contradiction by making the translation timing flexible rather than fixed.
2Productivity
If speech recognition frequency is increased to reduce delay, then translation timeliness is improved, but system complexity increases
Solution Approach 1:
The discriminant model serves as an intermediary component that sits between speech recognition and translation modules. It receives recognized text and provides guidance on when translation should occur, without requiring changes to the core speech recognition or translation engines. This intermediary approach improves timeliness by enabling more frequent translation opportunities while avoiding the need to fundamentally redesign or increase the complexity of the underlying recognition and translation systems.
3Loss of time
If translation is performed before full stops are encountered, then translation delay is reduced, but translation accuracy may deteriorate
Solution Approach 1:
The discriminant model provides feedback to the translation system about the likelihood of upcoming full stops. This feedback mechanism allows the system to make intelligent decisions: when the model indicates low probability of full stops, translation proceeds early with acceptable accuracy; when full stops are likely, the system waits for better translation opportunities. This feedback loop resolves the contradiction by using predictive information to guide translation timing decisions.
Data Source
AI summary
A method and apparatus for translating speech are provided. The method may include: recognizing received to-be-recognized speech of a source language to obtain a recognized text; concatenating the obtained recognized text after a to-be-translated text, to form a concatenated to-be-translated text; inputting the concatenated to-be-translated text into a pre-trained discriminant model to obtain a discrimination result for characterizing whether the concatenated to-be-translated text is to be translated, where the discriminant model is used to characterize a corresponding relationship between a text and a discrimination result corresponding to the text; in response to the positive discrimination result being obtained, translating the concatenated to-be-translated text to obtain a translation result of a target language, and outputting the translation result.