Neural Acoustic Model for Real-Time Speech Processing
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Solution Overview
Problem
Current speech processing systems lack human-level responsiveness and intelligence, struggling with real-time audio data processing on various computing devices and requiring significant resources, leading to high error rates and inefficiencies.
Innovation Solution
A neural network architecture comprising a convolutional neural network, a recurrent neural network, and a feed-forward neural network, with parallel data mappings and dimensionality reduction operations, allowing for efficient feature extraction and reduced computational complexity, enabling real-time processing on diverse devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex neural network model is used to improve speech processing accuracy, then classification accuracy is improved, but computational resource requirements increase and real-time processing becomes difficult
Solution Approach 1:
The neural network is divided into multiple independent layers (convolutional layer, recurrent layer, feed-forward layer) that process data in stages. Each layer performs a specific function and can be optimized independently, reducing the computational burden on any single component while maintaining overall accuracy.
Solution Approach 2:
The patent transforms the audio data into different dimensional representations through convolutional operations (spatial dimensions) and recurrent operations (temporal dimension). This multi-dimensional processing allows the network to extract features more efficiently than traditional single-dimensional approaches, improving accuracy without proportionally increasing computational complexity.
2Reliability
If traditional speech processing pipelines are used, then system stability is maintained, but error rates remain high and human-level responsiveness is not achieved
Solution Approach 1:
The patent combines three different neural network architectures (convolutional, recurrent, and feed-forward) into a unified composite model. Each architecture contributes its strengths: convolutional layers for local feature extraction, recurrent layers for temporal dependencies, and feed-forward layers for classification. This composite approach achieves human-level responsiveness while maintaining system stability through the complementary nature of the combined architectures.
3Measurement precision
If neural network models are trained with ample computing resources, then model accuracy is improved, but deployment on portable and embedded devices becomes challenging
Solution Approach 1:
The neural network architecture is designed to be dynamically adaptable to different computing environments. The model can be configured with varying numbers of layers, filters, and hidden units depending on the available resources. This dynamic configuration allows the same architectural framework to achieve high accuracy on powerful systems while being efficiently deployed on resource-constrained portable and embedded devices.
Data Source
AI summary
An audio processing system is described. The audio processing system uses a convolutional neural network architecture to process audio data, a recurrent neural network architecture to process at least data derived from an output of the convolutional neural network architecture, and a feed-forward neural network architecture to process at least data derived from an output of the recurrent neural network architecture. The feed-forward neural network architecture is configured to output classification scores for a plurality of sound units associated with speech. The classification scores indicate a presence of one or more sound units in the audio data. The convolutional neural network architecture has a plurality of convolutional groups arranged in series, where a convolutional group includes a combination of two data mappings arranged in parallel.