Accent-Aware Speech Recognition With Multi-Expert Modeling
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Solution Overview
Problem
Existing speech recognition systems face reduced performance due to the influence of different accents, particularly for Mandarin speakers whose pronunciation is affected by their native dialects, leading to inaccurate pronunciation and mispronunciation.
Innovation Solution
A speech recognition method that includes extracting speech features, performing accent feature recognition, and using a pre-trained model with an encoding layer, multi-expert network layer, and decoding layer to improve accuracy by recognizing accent features and adjusting model parameters through training with accent speech samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard speech recognition model is used, then the recognition process is simple and fast, but the recognition accuracy deteriorates when users speak with accents affected by their native dialects
Solution Approach 1:
The speech recognition model is segmented into three distinct functional layers: an encoding layer for extracting speech features, a multi-expert network layer for accent feature recognition and classification, and a decoding layer for text generation. This segmentation allows the system to handle accent variations specifically in the middle layer while maintaining efficient encoding and decoding processes, thereby improving accuracy without excessively complicating the overall structure.
Solution Approach 2:
The model employs dynamic adaptation mechanisms where the multi-expert network layer can dynamically adjust its processing based on the detected accent features. The system dynamically switches between different recognition pathways depending on the accent type detected, allowing the model to adapt its behavior to match the input speech characteristics, thus maintaining high accuracy across diverse accent conditions.
2Measurement precision
If the speech recognition model is trained with accent speech samples, then the recognition accuracy for accented speech improves, but the training time and computational resources increase
Solution Approach 1:
The model performs preliminary accent feature extraction and classification in the multi-expert network layer before proceeding to full speech recognition. By pre-identifying and categorizing accent features early in the processing pipeline, the system prepares the data in advance for more accurate recognition, reducing the need for extensive retraining and thereby decreasing overall training time while maintaining high accuracy.
Solution Approach 2:
The training process focuses computational resources on the multi-expert network layer where accent feature recognition occurs, rather than uniformly training all layers. This localized training approach concentrates effort on the specific component that handles accent variations, improving accent speech recognition accuracy while minimizing the total training time and computational resources required.
3Measurement precision
If accent feature recognition is performed as a separate step, then the recognition accuracy improves, but the processing time increases
Solution Approach 1:
The encoding layer, multi-expert network layer, and decoding layer are merged into a single integrated model structure that processes speech data in one continuous pipeline. The accent feature recognition is combined with the speech-to-text conversion process rather than being a separate post-processing step, allowing both functions to share computational resources and execute simultaneously, thus maintaining high accuracy without significantly increasing processing time.
Solution Approach 2:
The model maintains continuous processing flow where speech features extracted by the encoding layer are immediately passed to the multi-expert network layer for accent recognition, which in turn continuously feeds into the decoding layer for text generation. This continuous action eliminates idle time between processing stages, ensuring that the useful action of speech recognition proceeds without interruption, thereby maintaining high processing speed while achieving accurate accent recognition.
Data Source
AI summary
Embodiments of this specification provide a speech recognition method and apparatus. The speech recognition method includes: obtaining speech data to be recognized; extracting a speech feature in the speech data to obtain a first speech feature; performing accent feature recognition on the first speech feature to obtain a second speech feature carrying an accent feature; and recognizing first speech text content corresponding to the speech data based on the second speech feature. The accuracy and efficiency of speech recognition can be improved.