Mixed Language Speech Recognition Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech-to-text systems struggle with accurately recognizing mixed language speech, particularly challenging languages like Cantonese and English, and often incur high costs in terms of financial, computational, and time resources.
Innovation Solution
A system and method that generate enhanced mixed language datasets using simulated conversations between speakers, and employ a fidelity-accuracy-latency (FAL) evaluator to train a speech recognition model, improving its performance in recognizing mixed language words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing speech-to-text systems are used to recognize mixed language speech, then the system can process speech input, but the recognition accuracy deteriorates significantly
Solution Approach 1:
The system performs preliminary actions by generating synthetic mixed-language speech data before actual speech recognition training. The data generation module creates diverse mixed-language datasets with multiple language combinations and speaking styles, which are then used to pre-train the speech recognition model, improving its ability to handle real mixed-language speech accurately
Solution Approach 2:
The system changes parameters by adjusting language mixing ratios, speaking styles, and data augmentation parameters during the synthetic data generation process. The training module dynamically adjusts learning parameters and data sampling ratios to optimize recognition accuracy for different mixed-language scenarios
2Measurement precision
If high-quality mixed language training data is obtained through manual recording, then the data quality improves, but the time and cost resources increase significantly
Solution Approach 1:
The system uses copying by generating synthetic speech data that mimics real human speech patterns. The data generation module creates artificial mixed-language speech samples that replicate the characteristics of actual speech without requiring manual recording, thus maintaining data quality while eliminating time-consuming data collection processes
Solution Approach 2:
The system performs self-service by automatically generating its own training data through the data generation module. The module synthesizes mixed-language speech data using language models and speech synthesis techniques, eliminating the need for external data collection efforts and reducing both time and cost resources
3Ease of manufacture
If existing speech recognition models are trained on pure language data, then the training process is simple, but the model fails to recognize mixed language speech accurately
Solution Approach 1:
The system applies dynamics by making the training process adaptive and flexible. The training module dynamically adjusts the composition of training data, incorporating varying ratios of mixed-language samples based on the model's performance. The language mixing ratios and data sampling strategies are dynamically optimized during training to improve mixed-language recognition while maintaining training feasibility
Data Source
AI summary
Training a mixed language speech recognition model, and speech recognition performed by the model, can be enhanced. Model manager can control training and/or operation of the model. Mixed language data generation manager can generate an enhanced mixed language dataset that can enhance training of the model. Fine tuner can facilitate configuring hyperparameters of the model. Audio-based information and transcript that are representative of the dataset can be applied to the model to facilitate model training. FAL evaluator can determine fidelity, accuracy, and latency of performance of speech recognition on the audio-based information and transcript by the model. Based on such determination, mixed language data generation process and/or hyperparameters can be updated to enhance further training of the model to enhance fidelity, accuracy, and/or latency regarding performance of speech recognition by the model. Model manager can control one or more iterations of model training.