Advances in audio processing technologies like speech enhancement, audio compression, and hearing aids rely on automatic methods to evaluate whether processed audio sounds good to listeners. However, current computational approaches for assessing audio quality fail to match human perception, leading to systems that optimize for mathematical metrics rather than what actually sounds good to human ears. When engineers develop these technologies, existing evaluation metrics often disagree with human judgments, resulting in audio processing algorithms that may improve technical measurements while actually degrading the listening experience. This research will create computational tools that can automatically evaluate audio quality without requiring human listeners for every assessment, while maintaining strong agreement with human perception. The project develops new artificial intelligence models that learn to assess audio quality the way humans do, using novel machine learning training architectures and methodologies applied to human perceptual judgments across speech, music, and environmental sounds. These advancements will improve quality assessment for recorded speech, with direct applications in speech analysis and synthesis. This will ultimately lead to improvements in human language technologies such as speech enhancement, speaker extraction, and assistive hearing technologies which directly rely on perceptual quality assessments for improvements. They will also have a bro