Alternative Soft Labels for Compact Acoustic Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Training complex acoustic models for real-time speech recognition on resource-constrained devices like smartphones and tablets is hindered by computational limitations and latency issues, while compact models lack the analytical power of their wider counterparts.
Innovation Solution
Implementing knowledge distillation techniques to transfer training data from a high-resource teacher model to a lightweight student model using generated soft labels, which are derived from a soft label pool to enhance compact model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex acoustic models are used for real-time speech recognition, then recognition accuracy is improved, but computational resource requirements and latency increase
Solution Approach 1:
The patent creates a simplified student model that copies the essential recognition capabilities from a complex teacher model through knowledge distillation. The student model learns from the teacher's soft labels (probability distributions) rather than hard labels, capturing nuanced patterns while using fewer computational resources. This allows deployment on resource-constrained devices while maintaining acceptable accuracy.
Solution Approach 2:
The patent introduces soft labels as an intermediary between the teacher model's complex outputs and the student model's simpler processing requirements. These soft labels contain enriched information about class probabilities and relationships, serving as a bridge that transfers knowledge efficiently without requiring the student model to directly replicate the teacher's computational complexity.
2Measurement precision
If complex acoustic models are used for real-time speech recognition, then recognition accuracy is improved, but latency increases
Solution Approach 1:
The student model copies the teacher model's decision-making patterns in a compressed form, enabling faster inference. By learning from soft labels that encode probability distributions rather than requiring full complex model computation, the student model achieves real-time performance with reduced latency while preserving essential recognition accuracy.
Solution Approach 2:
The patent employs a lightweight student model that sacrifices some computational complexity for speed and efficiency. This simpler model is designed for rapid deployment on mobile devices, accepting minor accuracy trade-offs in exchange for significantly reduced latency and real-time capability.
3Use of energy by moving object
If compact models are used for real-time speech recognition, then computational constraints are satisfied, but analytical power decreases
Solution Approach 1:
The student model copies enriched knowledge from the teacher through soft labels, capturing complex patterns and relationships in a compact form. This allows the small model to achieve analytical power comparable to larger models by learning from the teacher's probability distributions rather than raw data.
Solution Approach 2:
The patent changes the training paradigm by using soft labels (probability distributions across multiple classes) instead of hard labels (single class assignments). This parameter change in the training data enables the compact student model to learn nuanced decision boundaries and relationships, significantly improving its analytical power relative to its size.
4Measurement precision
If knowledge distillation with soft labels is used to train student models, then compact model performance is improved, but additional parallel data is required
Solution Approach 1:
The teacher model performs preliminary processing to generate soft labels that encode rich information about class relationships and uncertainties. This preliminary action transforms raw data into enriched training signals before the student model training, allowing the student to learn more efficiently from condensed knowledge representations rather than requiring additional parallel training data.
Data Source
AI summary
An approach to identifying alternate soft labels for training a student model may be provided. A teaching model may generate a soft label for a labeled training data. The training data can be an acoustic file for speech or a spoken natural language. A pool of soft labels previously generated by teacher models can be searched at the label level to identify soft labels that are similar to the generated soft label. The similar soft labels can have similar length or sequence at the word phoneme, and/or state level. The identified similar soft labels can be used in conjunction with the generated soft label to train a student model.