Speaker Recognition Using Knowledge Distillation for Embedded Devices
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Solution Overview
Problem
Current speaker recognition systems, such as those using I-vectors, face challenges in scalability and accuracy with short vocal utterances, and the use of artificial neural networks (ANNs) is perceived as processor-intensive and requiring complex hardware, limiting their practicality for embedded devices.
Innovation Solution
A speaker recognition system employing a smaller artificial neural network (ANN) trained to emulate the output of a larger ANN, reducing the number of layers and parameters, and using knowledge distillation to compress the model, allowing for efficient processing and storage on low-power devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger artificial neural network is used for speaker recognition, then accuracy is improved, but processing requirements and hardware complexity increase
Solution Approach 1:
The patent creates a simplified copy (student network) that replicates the essential recognition capabilities of the larger teacher network. The student network is trained to emulate the teacher network's outputs, providing a lightweight alternative that maintains accuracy while reducing hardware requirements.
Solution Approach 2:
The patent transforms the neural network by reducing the number of layers and parameters from the teacher network to create the student network. This parameter reduction directly addresses the hardware complexity issue while the knowledge distillation process preserves the recognition accuracy.
2Measurement precision
If a larger artificial neural network is used for speaker recognition, then accuracy is improved, but processing time increases
Solution Approach 1:
The student network serves as a streamlined copy that processes speaker recognition queries faster than the full teacher network while maintaining comparable accuracy. This enables real-time or near-real-time processing on resource-constrained devices.
Solution Approach 2:
The patent segments the knowledge from the teacher network into a condensed form that can be processed more efficiently. By separating the training phase (where the teacher network operates) from the inference phase (where the student network operates), the system achieves both high accuracy and fast processing.
3Measurement precision
If a larger artificial neural network is used for speaker recognition, then accuracy is improved, but storage requirements increase
Solution Approach 1:
The student network is a compact copy that requires significantly less storage space than the teacher network. It captures the essential recognition patterns in a condensed format, reducing the storage burden on embedded devices while preserving accuracy.
Solution Approach 2:
By reducing the number of parameters and layers, the patent directly decreases the storage requirements. The knowledge distillation process ensures that this parameter reduction does not proportionally reduce accuracy, achieving efficient storage utilization.
4Measurement precision
If a larger artificial neural network is used for speaker recognition, then accuracy is improved, but energy consumption increases
Solution Approach 1:
The student network is a low-energy copy designed for deployment on power-constrained devices. It performs speaker recognition with minimal processor energy consumption while maintaining the accuracy benefits of neural network-based recognition.
Solution Approach 2:
The patent creates a dynamic system where the teacher network operates during training (when energy availability is less constrained) and the student network operates during deployment (when energy conservation is critical). This enables accurate recognition across different operational contexts.
Data Source
AI summary
A speaker recognition system comprises (i) at least one microphone operable to output data representing speech of a speaker and (ii) a controller. The controller is operable to: (a) receive the data output from the at least one microphone; (b) process the received data using a first artificial neural network to obtain first output data, the first artificial neural network having been trained based on outputs of a second artificial neural network, the second artificial neural network having been trained to perform speaker recognition; and (c) identify the speaker using the first output data. The first artificial neural network comprises fewer layers and/or fewer parameters than the second artificial neural network. The first artificial neural network is configured to emulate a result derivable using an output of the second artificial neural network.