Speaker identification, verification, and diarization using neural networks for conversational AI systems and applications
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Solution Overview
Problem
Existing speaker identification, verification, and diarization systems often require large neural network models that are inefficient and need specific training for each task, lacking compact architectures capable of handling multiple tasks effectively.
Innovation Solution
A neural network architecture that combines local and global temporal context processing using parallel branches with squeeze-and-excitation layers, enabling compact models for speaker identification, verification, and diarization, trained with loss functions like angular softmax margin loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large neural network models are used for speaker identification, verification, and diarization, then task performance is improved, but model size and computational complexity increase
Solution Approach 1:
The patent merges three separate neural network models (for speaker identification, verification, and diarization) into a single unified model. This unified model shares common layers and parameters across all three tasks, reducing overall model size and computational complexity while maintaining performance. The shared encoder processes speech inputs for all tasks, and task-specific outputs are generated through shared representations.
Solution Approach 2:
The unified neural network model is designed to perform multiple speaker-related tasks simultaneously (identification, verification, and diarization) through a single multi-functional architecture. The model uses task-independent shared layers that can be applied across different tasks, making the system more efficient and reducing the need for separate specialized models for each task.
2Measurement precision
If separate trained models are deployed for each speaker task, then task-specific accuracy is improved, but system complexity and training requirements increase
Solution Approach 1:
The patent implements a universal neural network model that can perform speaker identification, verification, and diarization tasks using a single trained model. The unified architecture processes all three tasks through shared representations, eliminating the need for separate trained models for each task while maintaining competitive accuracy across all tasks.
Solution Approach 2:
The unified model is segmented into task-independent shared layers and task-specific output layers. The shared encoder processes speech inputs and generates common representations that are then used by different task-specific modules. This segmentation allows the model to learn general speaker features that benefit all tasks while maintaining task-specific performance through specialized output layers.
3Reliability
If multiple task-specific models are trained and deployed, then specialized performance for each task is improved, but processing time and computational resources increase
Solution Approach 1:
The patent combines multiple task-specific processing pipelines into a single unified neural network model that processes speaker identification, verification, and diarization simultaneously. By sharing computational layers and representations across tasks, the system reduces redundant computations and processing time while maintaining specialized performance for each task through task-specific output modules.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker recognition, verification, and/or diarization. The techniques include applying a neural network (NN) to a speech data to obtain a speaker embedding representative of an association between the speech data and a speaker that produced the speech. The speech data includes a plurality of frames and a plurality of channels representative of spectral content of the speech data. The NN has one or more blocks of neurons that include a first branch performing convolutions of the speech data across the plurality of channels and across the plurality of frames and a second branch performing convolutions of the speech data across the plurality of channels. Obtained speaker embeddings may be used for various tasks of speaker identification, verification, and/or diarization.