Transform Domain Reconstruction for Noisy Speech Recognition
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Solution Overview
Problem
Automatic speech recognition (ASR) systems face challenges in noisy environments, where noise reduction techniques can corrupt the acoustic signal, leading to poor recognition accuracy, as they struggle to adapt recognition models from quiet environments to noisy conditions.
Innovation Solution
The technique involves transform domain reconstruction of noise-corrupted portions of an acoustic signal to emulate speech, using replacement transform values based on speech features like cepstral coefficients or probabilistic models, ensuring the reconstructed signal closely resembles natural speech for improved recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise reduction techniques strongly attenuate noise-corrupted portions of the acoustic signal, then voice quality from the listener's perspective is improved, but the transform domain representation becomes dissimilar to natural speech, causing feature extraction to corrupt recognition accuracy
Solution Approach 1:
Instead of suppressing noise-corrupted portions, the patent inverts the approach by reconstructing them to resemble natural speech. The system identifies noise-corrupted transform domain components and replaces them with synthesized components generated from speech portions, thereby improving both noise quality and preserving recognition accuracy.
Solution Approach 2:
The patent creates copies of speech portions to replace noise-corrupted portions. By extracting features from clean speech portions and synthesizing replacement transform values based on these speech characteristics, the system generates artificial speech segments that mimic natural speech, preventing feature corruption while maintaining voice quality.
2Measurement precision
If recognition models are retrained using new speech collected in noisy environments, then recognition accuracy in noisy conditions improves, but the process is time consuming and requires large amounts of new speech data
Solution Approach 1:
The patent performs preliminary action by reconstructing the acoustic signal in advance before feature extraction and recognition. By pre-processing the noisy signal to replace noise-corrupted portions with synthesized speech portions, the system prepares a cleaned signal that can be processed by existing recognition models without requiring retraining, thus saving time while maintaining accuracy.
Solution Approach 2:
The patent changes the parameters of the acoustic signal in the transform domain by replacing transform values of noise-corrupted portions with synthesized values. This parameter transformation occurs in the frequency domain, allowing the system to modify signal characteristics without changing the underlying recognition models, thereby avoiding time-consuming retraining while improving recognition performance.
3Object-affected harmful factors
If noise-corrupted portions are suppressed rather than reconstructed, then noise attenuation is achieved, but the reconstructed signal fails to resemble natural speech, worsening speech recognition performance
Solution Approach 1:
The patent introduces an intermediary process between noise suppression and feature extraction. Instead of directly suppressing noise-corrupted portions, the system uses speech portions as intermediaries to generate replacement transform values. These synthesized components act as mediators that preserve speech-like characteristics while removing noise, ensuring both noise attenuation and reliable speech recognition.
Data Source
AI summary
The present technology provides techniques for transform domain reconstruction of noise-corrupted portions of an acoustic signal to emulate speech which is obscured by the noise. Replacement transform values for the noise-corrupted portions are determined utilizing the portions of the acoustic signal which contain speech.