Audio Feature Encoding for Speech Recognition Efficiency
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Solution Overview
Problem
Traditional end-to-end speech recognition methods based on neural networks, such as RNNs and CNNs, face challenges due to high computational complexity, leading to low speech recognition efficiency.
Innovation Solution
The proposed audio information processing method involves obtaining a first audio feature, performing encoding on it based on adjacent features, and using the encoded features along with decoded text information to obtain text information, reducing computational complexity and improving efficiency by sequential encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If end-to-end speech recognition methods based on neural networks (RNN or CNN) are used, then the speech recognition process is simplified and text information can be obtained directly from audio information, but the computational complexity increases leading to low speech recognition efficiency
Solution Approach 1:
The patent segments the audio feature processing into multiple discrete time steps, where each step processes a specific time point's audio feature independently. This segmentation allows the model to process audio features in a sequential manner rather than requiring complex global computations, thereby reducing computational complexity while maintaining the end-to-end processing capability.
2Reliability
If traditional speech recognition methods based on ASR technology are used, then speech recognition can be achieved, but the training process becomes cumbersome and requires introduction of lots of artificially defined prior knowledge
Solution Approach 1:
The patent extracts and eliminates the need for artificially defined prior knowledge and separate acoustic model and language model training processes. By using a neural network that directly maps audio features to text information without requiring pre-defined linguistic rules or separate modeling stages, the invention simplifies the training process while maintaining reliable speech recognition capability.
3Measurement precision
If acoustic model and language model are trained separately in traditional speech recognition methods, then each model can be optimized independently, but joint optimization benefits are not achieved and the overall system complexity increases
Solution Approach 1:
The patent merges the functions of the acoustic model and language model into a single neural network framework. This unified model processes audio features and generates text information in an integrated manner, achieving joint optimization of both functions while reducing the overall system structure complexity compared to separate model training approaches.
Data Source
AI summary
In various embodiments, this application provides an audio information processing method, an audio information processing apparatus, an electronic device, and a storage medium. An audio information processing method in an embodiment includes: obtaining a first audio feature corresponding to audio information; performing, based on an audio feature at a specified moment in the first audio feature and audio features adjacent to the audio feature at the specified moment, an encoding on the audio feature at the specified moment to obtain a second audio feature corresponding to the audio information; obtaining decoded text information corresponding to the audio information; and obtaining, based on the second audio features and the decoded text information, text information corresponding to the audio information. According to this method, fewer parameters are used in the process of obtaining the second audio feature and obtaining, based on the second audio feature and the decoded text information, the text information corresponding to the audio information, thereby reducing computational complexity in the audio information processing process and improving audio information processing efficiency.