User Adaptive Speech Recognition via Scaling Factors
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Solution Overview
Problem
Existing speech recognition technologies face challenges in accurately recognizing speech due to individual user characteristics, such as dialects and pronunciation habits, which can reduce recognition accuracy and make it difficult to use a model trained on one user for others.
Innovation Solution
A processor-implemented speech recognition method that extracts an identity vector from user speech data, uses a sub-neural network to determine a scaling factor, and applies it to a main neural network to adjust hidden states or outputs, allowing for user-adaptive recognition by reflecting individual characteristics in the recognition result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a speech recognition model is trained on general speech data without user-specific adaptation, then the model can be applied to multiple users, but recognition accuracy deteriorates due to individual user characteristics such as dialects and pronunciation habits
Solution Approach 1:
The speech recognition system is segmented into two distinct neural networks: a sub-neural network that processes identity vectors to capture user-specific characteristics, and a main neural network that performs the actual speech recognition. This segmentation allows the system to separately handle user adaptation and speech recognition tasks, resolving the contradiction between versatility and accuracy.
Solution Approach 2:
An identity vector is introduced as an intermediary element that carries user-specific information from the sub-neural network to the main neural network. This intermediary enables the main network to adapt to individual users without requiring complete retraining, thus maintaining both versatility across users and accuracy for each user.
2Measurement precision
If a speech recognition model is trained specifically for one user, then recognition accuracy improves for that user, but the model cannot be effectively used for other users
Solution Approach 1:
The system dynamically adapts to different users by processing identity vectors through the sub-ne neural network to generate user-specific scaling factors. This dynamic adaptation mechanism allows the main neural network to adjust its behavior based on the current user's characteristics while maintaining the same underlying model structure, enabling both high accuracy and reusability.
Solution Approach 2:
The system changes parameters (scaling factors) based on user identity rather than changing the entire model structure. The sub-neural network extracts user-specific parameters from identity vectors, and these parameters are applied to modulate the main neural network's processing. This parameter-based adaptation allows the same model to achieve high accuracy for different users without requiring retraining.
3Measurement precision
If user-specific adaptation is implemented through traditional methods, then recognition accuracy improves, but system complexity increases significantly
Solution Approach 1:
Instead of creating and training separate models for each user, the system uses a copying approach where a single main neural network model is reused for all users. The user-specific adaptation is achieved by copying and applying user-specific parameters (scaling factors) generated by the sub-neural network, rather than copying entire model structures. This dramatically reduces system complexity while maintaining accuracy.
Data Source
AI summary
A user adaptive speech recognition method and apparatus are provided. A speech recognition method includes extracting an identity vector representing an individual characteristic of a user from speech data, implementing a sub-neural network by inputting a sub-input vector including at least the identity vector to the sub-neural network, determining a scaling factor based on a result of the implementing of the sub-neural network, implementing a main neural network, configured to perform a recognition operation, by applying the determined scaling factor to the main neural network and inputting the speech data to the main neural network to which the determined scaling factor is applied, and indicating a recognition result of the implementation of the main neural network.