Speech Recognition Model Training with CIF-CTC Joint Loss
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Solution Overview
Problem
Existing speech recognition models face challenges in effectively applying multi-task learning knowledge due to limitations in encoding data, leading to inefficiencies in training and accuracy.
Innovation Solution
A method involving a speech recognition model with a Continuous Integrate-and-Fire (CIF) prediction sub-model and a Connectionist Temporal Classification (CTC) prediction sub-model is used to encode training speech data, obtain acoustic vectors, information amounts, and target probabilities, determining a target loss to update model parameters, ensuring consistent application of multi-task learning knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-task learning method is used to train the speech recognition model, then the accuracy of the speech recognition model is improved, but the training efficiency is affected due to limited application scope of learned knowledge
Solution Approach 1:
The patent applies multi-task learning by integrating multiple prediction tasks (alignment prediction, CTC prediction, and information amount prediction) into a single unified model. The encoder shares common features across all tasks, allowing the model to learn universal representations that improve accuracy while maintaining training efficiency through joint optimization.
Solution Approach 2:
The patent segments the speech recognition model into distinct functional components: an encoder for feature extraction, multiple prediction sub-models (alignment, CTC, information amount) for different prediction tasks, and a loss calculation module. This segmentation allows each component to be optimized independently while contributing to the overall model performance.
2Adaptability or versatility
If prediction knowledge based on alignment algorithm acts on encoding module, then the knowledge can be applied in encoding process, but it is difficult to realize comprehensive application of multi-task learning knowledge
Solution Approach 1:
The encoder serves multiple functions by providing shared features to three different prediction sub-models (alignment prediction, CTC prediction, and information amount prediction). This universal feature extraction capability allows the learned knowledge to be comprehensively applied across different prediction tasks without significantly increasing model complexity.
Solution Approach 2:
The patent introduces an information amount prediction sub-model as an intermediary that bridges the alignment prediction and CTC prediction tasks. This intermediate component processes the encoded features and provides additional supervisory signals, enabling comprehensive application of multi-task learning knowledge while maintaining a structured and manageable model architecture.
Data Source
AI summary
A model training method, a speech recognition method and apparatus, a medium, and a device are provided. The speech recognition model including an encoder, a CIF prediction sub-model and a CTC prediction sub-model. The model training method includes: encoding training speech data based on the encoder to obtain an acoustic vector sequence corresponding to the training speech data; obtaining an information amount sequence corresponding to the training speech data based on the acoustic vector sequence and the CIF prediction sub-model; obtaining a target probability sequence based on the acoustic vector sequence and the CTC prediction sub-model; determining a target loss of the speech recognition model based on the information amount sequence and the target probability sequence; and updating, in response to an updating condition being satisfied, a model parameter of the speech recognition model based on the target loss.