Real-Time Cepstral Variance Normalization for Audio Feature Extraction
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Solution Overview
Problem
Existing speech recognition systems face latency issues due to the need for buffering entire audio utterances and high memory requirements, which hinder real-time processing and user experience, especially in portable devices.
Innovation Solution
Implementing real-time cepstral variance normalization (CVN) that updates mean and variance estimates frame-wise without buffering, allowing for immediate analysis of audio features using equations that adjust previous values with new data, reducing latency and memory needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition systems buffer entire audio utterances for analysis, then measurement precision of speech features is improved, but loss of time (latency) increases and device complexity increases
Solution Approach 1:
The patent segments the audio processing into frame-wise operations, computing cepstral mean subtraction and variance normalization incrementally for each frame rather than waiting for the complete utterance. This allows real-time processing with reduced latency while maintaining feature accuracy through progressive updates of statistical parameters.
Solution Approach 2:
The system performs preliminary computation of mean and variance statistics as audio frames arrive, preparing normalization parameters in advance before complete utterance analysis is needed. This preliminary action enables faster subsequent processing and reduces overall latency.
2Measurement precision
If traditional speech recognition systems buffer entire audio utterances, then measurement precision is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent divides the audio processing into manageable frames processed sequentially, maintaining only incremental statistical parameters (mean and variance) in memory rather than buffering entire utterances. This segmentation reduces memory requirements while preserving measurement precision through frame-wise feature extraction.
Solution Approach 2:
The system extracts only the essential statistical parameters (cepstral mean and variance) needed for normalization, discarding the need to retain complete audio buffers. This extraction approach maintains feature accuracy while significantly reducing memory complexity.
3Loss of time
If real-time processing is implemented without buffering, then loss of time is reduced, but measurement precision of speech features deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where previously computed mean and variance parameters are continuously updated with new frame data. This incremental feedback approach maintains statistical accuracy for real-time normalization, ensuring measurement precision is preserved even as processing occurs without complete buffering.
Solution Approach 2:
The system dynamically updates normalization parameters (mean and variance) as each new audio frame arrives, adapting statistical estimates in real-time. This dynamic approach maintains feature accuracy while enabling low-latency processing without static pre-computation requirements.
Data Source
AI summary
Cepstral variance normalization is described for audio feature extraction. In some embodiments a method includes receiving a sequence of frames of digitized audio from a microphone, determining a feature vector for a first frame of the sequence of frames, the feature vector being determined using an initial mean and an initial variance, updating the initial mean to a current mean using the determined feature vector for the first frame, updating the variance to a current variance using the current mean and the determined feature vector for the first frame, determining a next feature vector for each of subsequent frames of the sequence of frames, after determining a next feature vector for each subsequent frame, updating the current mean to a next current mean and updating the current variance to a next current variance and wherein determining a feature vector for a subsequent frame comprises using the next current mean and the next current variance, and sending the determined feature vectors to a recognition stage of an audio processing pipeline.