Neural Modulation Codes for Multilingual Speech Recognition
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Solution Overview
Problem
Automatic Speech Recognition (ASR) systems face challenges in achieving optimal performance due to variability in human speech, such as language, accent, and emotional factors, leading to a trade-off between specificity and training data, resulting in suboptimal performance and increased software complexity.
Innovation Solution
A neural modulation approach that uses codes to alter the behavior of connections in a main task-performance network, allowing for precise modeling of conditions without the need for extensive adaptation data or time, enabling competitive performance across multiple languages and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate condition-dependent systems are trained for each language, accent, or speaker profile, then recognition precision is improved, but device complexity and maintenance effort increase
Solution Approach 1:
The patent merges multiple condition-dependent systems into a single unified neural network by introducing condition codes that represent different languages, accents, and speaker profiles. These codes modulate the network's behavior dynamically, allowing the system to switch between different recognition modes without requiring separate software modules for each condition.
Solution Approach 2:
The system dynamically adapts its recognition behavior based on input condition codes. The neural network uses these codes to modulate its internal representations and processing pathways, enabling it to optimize performance for specific languages, accents, or speaker profiles on-the-fly without requiring separate static systems for each condition.
2Device complexity
If mixed condition approaches are used with speaker-independent acoustic modeling, then device complexity is reduced, but manufacturing precision and recognition accuracy deteriorate
Solution Approach 1:
The patent applies local quality by allowing different parts of the neural network to be selectively modulated by condition codes. Specific layers or pathways within the network can be enhanced or suppressed based on the detected language, accent, or speaker profile, enabling high accuracy for specific conditions while maintaining a unified system architecture.
3Measurement precision
If individual systems are trained for each language, then recognition precision is improved, but loss of time and data collection effort increase
Solution Approach 1:
The patent creates a universal neural network system that can handle multiple languages, accents, and speaker profiles through a single model. The condition codes enable this unified system to achieve performance comparable to individual language-specific systems, eliminating the need to collect and train separate datasets for each language or accent.
4Measurement precision
If language-dependent systems are deployed for all languages, then recognition precision is improved, but device complexity and resource requirements increase
Solution Approach 1:
The system uses condition codes as parameters to dynamically change the network's behavior. By encoding language, accent, and speaker profile information into these codes, the system can adapt its processing to achieve high precision for any supported condition without requiring separate system deployments, thereby scaling precision across diverse languages efficiently.
Data Source
AI summary
Computer-implemented methods and apparatus that use neural modulation codes as an alternative to training many individual recognition models or to loosing performance by training mixed models. Large neural models are modulated by codes that model the different conditions. The codes directly alter (modulate) the behavior of connections in a multiconditional perceptual classifier, so as to permit the most appropriate neuronal units and their features to be applied to each condition. The approach may be applied to multilingual ASR, where the resulting multilingual network arrangement is able to achieve performance that is competitive or better than individually trained mono-lingual network. Moreover, the approach requires no adaptation data or extensive adaptation/training time to operate in a manner tuned to each condition. Beyond multilingual speech processing systems the approach can be applied to many other perceptual processing problems (e.g. speech recognition, speech synthesis, language translation, image processing) to factor the processing task from the conditioning variables that drive the actual realization. Instead of adapting or retraining neural systems to individual conditions, it modulates a large invariant network to operate in different modes based on conditioning codes that are provided by auxiliary networks that model these conditions.