Curriculum Learning for Speech Recognition Training
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Solution Overview
Problem
Current speech recognition technologies using Stochastic Gradient Descent (SGD) for training neural networks face inefficiencies, including prolonged training times and the risk of getting stuck in local optima, leading to degraded accuracy.
Innovation Solution
Implementing a curriculum learning approach that orders training samples based on difficulty, starting with easier samples and progressing to more challenging ones, using curriculum functions such as sound quality and task difficulty to guide the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If Stochastic Gradient Descent (SGD) is used to train neural networks with billions of samples, then the training can be parallelized and processed with fast processors, but the training time is still prolonged and the risk of getting stuck in local optima increases
Solution Approach 1:
The patent applies preliminary action by pre-ordering training samples based on difficulty levels before training begins. Samples are sorted from easiest to most difficult, and the neural network progresses through this predetermined sequence. This preliminary organization of training data allows the system to start with simpler patterns that build foundational knowledge, then gradually introduce more complex patterns, thereby reducing overall training time compared to random sampling while maintaining efficient parallel processing capability.
2Power
If Stochastic Gradient Descent (SGD) is used to train neural networks, then the processing can be parallelized, but the accuracy degrades due to getting stuck in local optima
Solution Approach 1:
The patent applies preliminary action by pre-ordering training samples based on difficulty levels before training begins. Samples are sorted from easiest to most difficult, and the neural network progresses through this predetermined sequence. This preliminary organization of training data allows the system to start with simpler patterns that build foundational knowledge, then gradually introduce more complex patterns, thereby reducing overall training time compared to random sampling while maintaining efficient parallel processing capability.
Solution Approach 2:
The patent applies parameter changes by systematically varying the difficulty parameter of training samples throughout the training process. The curriculum function dynamically adjusts which samples are presented based on their difficulty characteristics, transforming the training process from static random sampling to a dynamic progression through difficulty levels. This parameter-based organization helps the network escape local optima by ensuring it encounters samples in an optimal sequence that progressively challenges its capabilities.
3Ease of operation
If training samples are presented in random order using SGD, then the training process is simple to implement, but the training speed is reduced and accuracy is degraded
Solution Approach 1:
The patent applies preliminary action by pre-ordering training samples based on difficulty levels before training begins. Samples are sorted from easiest to most difficult, and the neural network progresses through this predetermined sequence. This preliminary organization of training data allows the system to start with simpler patterns that build foundational knowledge, then gradually introduce more complex patterns, thereby reducing overall training time compared to random sampling while maintaining efficient parallel processing capability.
Solution Approach 2:
The patent applies self-service by implementing an automatic curriculum learning system that uses a curriculum function to autonomously determine the optimal ordering of training samples. The system evaluates sample characteristics and automatically sequences them by difficulty without requiring manual intervention. This self-organizing capability maintains ease of operation while dramatically improving training speed and accuracy compared to both random ordering and manually-crafted curricula.
4Productivity
If training samples are ordered by difficulty using curriculum learning, then training speed improves and accuracy increases, but the device complexity increases
Solution Approach 1:
The patent applies self-service by implementing an automatic curriculum learning system that uses a curriculum function to autonomously determine the optimal ordering of training samples. The system evaluates sample characteristics and automatically sequences them by difficulty without requiring manual intervention. This self-organizing capability maintains ease of operation while dramatically improving training speed and accuracy compared to both random ordering and manually-crafted curricula.
Solution Approach 2:
The patent applies parameter changes by systematically varying the difficulty parameter of training samples throughout the training process. The curriculum function dynamically adjusts which samples are presented based on their difficulty characteristics, transforming the training process from static random sampling to a dynamic progression through difficulty levels. This parameter-based organization helps the network escape local optima by ensuring it encounters samples in an optimal sequence that progressively challenges its capabilities.
Data Source
AI summary
Methods and apparatus related to training speech recognition devices are presented. A computing device receives training samples for training a neural network to learn an acoustic speech model. A curriculum function for speech modeling can be determined. For each training sample of the training samples, a corresponding curriculum function value for the training sample can be determined using the curriculum function. The training samples can be ordered based on the corresponding curriculum function values. In some embodiments, the neural network can be trained utilizing the ordered training samples. The trained neural network can receive an input of a second plurality of samples corresponding to human speech, where the second plurality of samples differs from the training samples. In response to receiving the second plurality of samples, the trained neural network can generate a plurality of phones corresponding to the captured human speech.