Neural Network Voice Activity Detection in Noisy Audio
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately detecting voice activity in audio inputs, particularly in noisy conditions, which affects the performance of speech recognition systems.
Innovation Solution
A neural network-based voice activity detection system is trained using labeled audio waveforms to classify input audio as containing speech or not, employing a combination of convolutional, long short-term memory, and fully connected deep neural network layers to process audio waveforms and generate accurate classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional voice activity detection methods are used, then the system is simpler to implement, but the detection accuracy deteriorates in noisy conditions
Solution Approach 1:
The audio waveform is divided into multiple frames, with each frame processed independently through the neural network to generate voice activity predictions. This segmentation allows the system to handle noisy conditions frame-by-frame while maintaining overall detection accuracy through temporal aggregation of results.
Solution Approach 2:
The system transforms the one-dimensional audio waveform into a two-dimensional representation by creating a spectrogram or feature matrix across time and frequency dimensions. This dimensional transformation enables the neural network to capture both temporal and spectral characteristics of speech, significantly improving detection accuracy in noisy environments.
2Reliability
If voice activity detection is performed continuously, then speech recognition performance is improved, but computational energy consumption increases
Solution Approach 1:
The system extracts only the relevant portions of audio containing voice activity using the trained neural network, rather than processing the entire audio stream continuously. By identifying and extracting speech segments based on voice activity detection, the system reduces computational energy consumption while maintaining speech recognition performance.
Solution Approach 2:
The neural network performs voice activity detection at selected intervals or on sampled frames rather than analyzing every single audio sample. This partial action approach provides sufficient detection accuracy to trigger speech recognition only when needed, reducing overall computational energy consumption while maintaining reliability.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting voice activity. In one aspect, a method include actions of receiving, by a neural network included in an automated voice activity detection system, a raw audio waveform, processing, by the neural network, the raw audio waveform to determine whether the audio waveform includes speech, and provide, by the neural network, a classification of the raw audio waveform indicating whether the raw audio waveform includes speech.