Multi-Type Acoustic Feature Integration via Deep Neural Networks
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Solution Overview
Problem
Current speech recognition technologies face challenges in stability and adaptability due to the use of single-type acoustic features, leading to decreased recognition accuracy and robustness in complex environments, where factors like distance, location, and speaker conditions affect performance.
Innovation Solution
A multi-type acoustic feature integration method using deep neural networks that extracts and integrates multiple types of acoustic features through subnetwork branches, attention mechanisms, and dimension reduction or normalization, to enhance recognition accuracy and stability by exploring internal correlations between features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only a single type of acoustic feature is extracted from speech samples, then the system complexity is reduced, but the recognition accuracy and robustness deteriorate in complex environments
Solution Approach 1:
The patent merges multiple types of acoustic features (MFCC, PLP, spectrogram, chroma) into a unified deep neural network framework. The feature integration network combines these different feature types through concatenation and accumulation operations, allowing the system to leverage the complementary information from each feature type to improve recognition accuracy while maintaining manageable complexity through automated feature selection mechanisms.
Solution Approach 2:
The deep neural network architecture provides universal functionality by handling multiple acoustic feature types through a single integrated model. The network can process different feature types (MFCC, PLP, spectrogram, chroma) and adapt to various speech tasks, making the system multi-functional and capable of performing speech recognition, speaker verification, and other speech-related tasks with high accuracy.
2Reliability
If multiple types of acoustic features are integrated into the deep neural network, then the recognition accuracy and stability are improved, but the device complexity increases
Solution Approach 1:
The patent segments the acoustic feature integration process into distinct components: MFCC extraction module, PLP extraction module, spectrogram extraction module, and chroma extraction module. Each module processes a specific feature type independently before the features are combined in the feature integration network. This segmentation allows for modular design and easier management of complexity while maintaining the benefits of multi-feature integration.
Solution Approach 2:
The feature integration network acts as an intermediary between the different acoustic feature extraction modules and the final speech task processing. This intermediary component consolidates and harmonizes the information from multiple feature types, preparing it for optimal processing by the speech recognition or verification system. The intermediary structure simplifies the overall system architecture by providing a standardized interface for feature integration.
3Adaptability or versatility
If deep neural networks are trained with multi-type acoustic features, then the adaptability to different speech tasks is improved, but the training complexity and computational resources increase
Solution Approach 1:
The patent implements dynamic adaptability by allowing the deep neural network to automatically adjust its feature selection and weighting based on the specific speech task at hand. The system can dynamically reconfigure which acoustic features are most relevant for a given task, enabling high adaptability across different speech applications without requiring manual retraining for each task. This dynamic behavior is achieved through learned feature importance weights and adaptive network configurations.
Solution Approach 2:
The system utilizes parameter changes during training to optimize the network's adaptability. By adjusting learning rates, feature weighting parameters, and network architecture parameters during the training process, the deep neural network learns to efficiently represent different speech tasks using the same multi-feature framework. This parameter optimization enables the system to adapt to various speech tasks without proportionally increasing training complexity.
Data Source
AI summary
The application discloses a multi-type acoustic feature integration method and system based on deep neural networks. The method and system include using labeled speech data set to train and build a multi-type acoustic feature integration model based on deep neural networks, to determine or update the network parameters of the multi-type acoustic feature integration model; the method and system includes inputting the multiple types of acoustic features extracted from the testing speech into the trained multi-type acoustic feature integration model, and extracting the deep integrated feature vectors in frame level or segment level. The solution supports the integrated feature extraction for multiple types of acoustic features in different kinds of speech tasks, such as speech recognition, speech wake-up, spoken language recognition, speaker recognition, and anti-spoofing etc. It encourages the deep neural networks to explore internal correlation between multiple types of acoustic features according to practical speech tasks, to improve the recognition accuracy and stability of speech applications.