Neural Network Memory for Audio Prosody
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Solution Overview
Problem
Current speech processing neural networks rely on the assumption that a single sentence contains enough information to determine prosody, ignoring additional contextual information, and lack explicit components to model context beyond the sentence, limiting their capacity.
Innovation Solution
The implementation of neural network memory systems with multiple memory types, including short-term, episodic, and semantic memory, to retain and utilize contextual information over time, allowing for more accurate prosody determination in speech processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single sentence is used to determine prosody, then the processing speed is fast, but the accuracy of prosody determination deteriorates due to insufficient contextual information
Solution Approach 1:
The memory system is segmented into three distinct types: short-term memory for immediate contextual information, episodic memory for past experiences and events, and semantic memory for general knowledge and facts. This segmentation allows the system to access different types of contextual information appropriately, improving prosody determination accuracy without creating a monolithic complex structure
Solution Approach 2:
The memory system is designed with multi-functionality where each memory type serves multiple purposes. For example, short-term memory stores both linguistic context and speaker state information, while episodic memory captures both narrative events and emotional contexts. This universal design improves prosody accuracy by providing comprehensive contextual information while avoiding the need for separate specialized systems
2Measurement precision
If contextual information beyond single sentence is modeled, then the accuracy of prosody determination is improved, but the device complexity increases due to lack of explicit components
Solution Approach 1:
The neural network structure is segmented into distinct memory modules (short-term, episodic, semantic) that can be independently managed and accessed. This segmentation provides explicit structural components for modeling long-range context, improving prosody accuracy without requiring a monolithic complex network architecture
Solution Approach 2:
The memory system acts as an intermediary between the input text and the prosody generation process. Instead of directly processing long-range dependencies through complex network connections, the system uses memory modules as mediators to store and retrieve contextual information, simplifying the overall network structure while maintaining high accuracy
3Loss of information
If multiple types of memory are implemented, then the capacity to retain contextual information is improved, but the ease of operation deteriorates due to management complexity
Solution Approach 1:
Each memory type is equipped with its own manager module that autonomously handles storage, retrieval, and update operations. The short-term memory manager, episodic memory manager, and semantic memory manager independently manage their respective memory types, reducing the operational burden on the overall system while ensuring comprehensive information retention
Data Source
AI summary
Techniques for utilizing memory for a neural network are described. For example, some techniques utilize a plurality of memory types to respond to a query from a neural network including a short-term memory to store fine-grained information for recent text of a document and receiving a first value in response, an episodic long-term memory to store information discarded from the short-term memory in a compressed form and receiving a second value in response, and a semantic long-term memory to store relevant facts per entity in the document.