Ubiquitous acoustic sensors and actuators, i.e., microphones and loudspeakers, are among the most common components in consumer electronic devices. Traditionally, these components have been primarily used for sound-related tasks, including voice-user interfaces, sound playback, and sound event detection. However, with the growing demand for consumer electronics to deliver more intelligent, cost-effective, human-centric, and trustworthy ambient intelligence while reducing costs, energy consumption, and computational overhead, there is an increasing need to unlock the full potential of these components for cross-modality sensing applications. The dissertation argues that, by designing advanced signal processing techniques and deep learning models, I am able to repurpose acoustic components for cross-modality sensing applications, achieving resolutions comparable to dedicated sensors. To support this, I develop end-to-end systems that encompass hardware design, sensor placement optimization, advanced signal processing, deep neural network architectures, and system-level optimizations. By leveraging a thorough understanding of the strengths and limitations of acoustic sensors and actuators, this dissertation reveals novel functionalities in ubiquitous acoustic devices, including speech privacy protection, speech enhancement, and monitoring of daily life and health. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]