Wavelet Feature Compression for Distributed Speech Recognition Bandwidth
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Solution Overview
Problem
Current distributed speech recognition systems face challenges in achieving robustness and efficient data transmission due to limitations in computing power, memory, and noise interference, with existing methods failing to effectively compress speech features while maintaining recognition performance.
Innovation Solution
The proposed method, 'Suppression by Selecting Wavelets' (SSW), employs discrete wavelet transform (DWT) to filter speech features into low-frequency and high-frequency components, transmitting only the low-frequency components for compression and normalization, and reconstructing features using inverse DWT with a post-filter to alleviate over-smoothing, thereby enhancing noise robustness and reducing data transmission bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech features are transmitted at full resolution for accurate recognition, then recognition performance is maintained, but data transmission bandwidth increases
Solution Approach 1:
The speech feature spectrum is segmented into multiple frequency bands using filter banks. Only the most discriminative frequency bands are selected and transmitted to the server, while less important bands are discarded or coarsely quantized. This segmentation allows the system to maintain recognition performance on critical bands while reducing overall data transmission bandwidth.
Solution Approach 2:
The system extracts and transmits only the essential speech feature components that are most important for recognition accuracy. By identifying and removing redundant or less discriminative features before transmission, the system achieves bandwidth reduction while preserving the core information needed for accurate speech recognition.
2Productivity
If speech features are compressed to reduce bandwidth, then data transmission efficiency improves, but recognition performance degrades
Solution Approach 1:
Different compression strategies are applied to different frequency bands based on their importance for recognition. Critical frequency bands maintain higher fidelity with minimal compression, while less critical bands undergo more aggressive compression. This local differentiation of quality levels allows the system to optimize the balance between transmission efficiency and recognition performance.
Solution Approach 2:
The system dynamically adjusts compression parameters such as quantization bit depth, frame rate, and frequency band selection based on network conditions, speech content, and recognition requirements. By changing these parameters adaptively, the system can maintain acceptable recognition performance while optimizing data transmission efficiency for different operating scenarios.
3Measurement precision
If noise robustness techniques are applied to maintain performance in noisy environments, then recognition accuracy improves, but computational complexity increases
Solution Approach 1:
Noise robustness processing is performed preliminarily on the client device before speech features are transmitted to the server. By applying noise suppression, spectral subtraction, or other robustness techniques locally on the mobile device, the system prepares cleaner speech features for transmission, reducing the computational burden on the server while maintaining recognition accuracy in noisy environments.
Solution Approach 2:
The client device acts as an intermediary that performs preliminary noise robustness processing on speech signals before transmission. This intermediary processing step cleanses the speech features of noise artifacts, allowing the server to focus on recognition tasks with already-preprocessed data, thereby distributing computational complexity favorably across the client-server architecture.
Data Source
AI summary
A system is provided to realize suppression by selecting wavelets for feature compression in distributed speech recognition. The system comprises a first device and a second device. The first device comprising: a first network module for connecting to a network; an acoustic transducer module for recording speech and outputting frames of recorded signal; and a first processor configured for the following: extracting multiple-dimensional speech features from the frames of the recorded signal to generate multiple feature sequences; applying discrete wavelet transform (DWT) to the feature sequences to obtain a plurality of component data; and transmitting at least one of the plurality of component data via the network, wherein another one of the plurality of component data is not transmitted. The second device comprising: a second network module for connecting to the network and receiving the at least one of the plurality of component data from the first device; and a second processor configured for the following: updating the received data to generate an updated data; and applying inverse discrete wavelet transform (IDWT) to the updated data to obtain reconstructed speech data.