AI Speech Recognition Dynamic Weight Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech recognition systems fail to deliver optimal performance, especially in noisy environments, due to fixed weights for acoustic and language models, which affect recognition accuracy.
Innovation Solution
An AI device dynamically adjusts the weight of the acoustic model based on the input speech signal, using noise signal classification probabilities and confidence levels to determine optimal weights for each unit frame, thereby improving speech recognition performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed weights are applied to acoustic model and language model, then system complexity is reduced, but speech recognition performance deteriorates in noisy environments
Solution Approach 1:
The patent implements dynamic weight adjustment for the acoustic model by introducing a weight determination module that calculates optimal weights based on noise signal classification probabilities and confidence levels. The weight varies over time according to the input speech signal characteristics, transforming the static weight system into a dynamic one that adapts to changing environmental conditions, thereby resolving the contradiction between system complexity and recognition performance.
Solution Approach 2:
The patent changes the parameter (weight) of the acoustic model based on environmental conditions. By calculating noise probabilities and confidence levels, the system dynamically adjusts the acoustic model weight parameter to optimize speech recognition performance in different noise conditions, rather than using a fixed weight parameter.
2Measurement precision
If acoustic model weight is increased, then speech recognition accuracy improves in clear environments, but performance deteriorates in noisy environments
Solution Approach 1:
The system dynamically adjusts the acoustic model weight based on real-time noise assessment. In clear environments, the weight is increased to improve accuracy, while in noisy environments, the weight is decreased to prevent degradation, making the system adaptable to different environmental conditions through dynamic parameter adjustment.
Solution Approach 2:
The acoustic model weight parameter is changed according to environmental conditions. The weight determination module calculates optimal weights based on noise probabilities and confidence levels, allowing the parameter to vary between high values (for clear environments) and low values (for noisy environments), thus achieving both high accuracy and environmental adaptability.
3Reliability
If dynamic weight adjustment is implemented, then speech recognition performance improves in varying conditions, but device complexity increases
Solution Approach 1:
The weight determination process is segmented into distinct functional modules: a noise signal classification module that calculates noise probabilities, a confidence level calculation module that assesses acoustic model reliability, and a weight determination module that synthesizes these inputs. This segmentation manages complexity by breaking down the dynamic adjustment process into manageable, specialized components.
Solution Approach 2:
The patent introduces intermediary elements (noise probability calculations and confidence level assessments) that mediate between the input speech signal and the final weight determination. These intermediaries provide structured information processing that manages system complexity while enabling sophisticated dynamic weight adjustment based on environmental conditions.
Data Source
AI summary
An artificial intelligence (AI) device may acquire a probability that a received speech signal is classified as a noise signal, calculate a confidence level of a first model for determining to which phoneme the speech signal belongs, based on the speech signal, determine a weight of the first model based on the probability and the confidence level of the first model, and output a speech recognition result of the speech signal using the determined weight of the first model.