Spoken language technologies, such as automatic speech recognition and synthesis, typically treat speech as a string of "phones". In contrast, humans produce speech through a complex combination of semi-independent articulatory trajectories. Recent theories of phonology acknowledge this, and treat speech as a combination of multiple streams of linguistic "features". In this talk I will present ways in which the factorization of speech into features can be useful in speech recognition, in both audio and visual (lipreading) settings. The main contribution is a feature-based approach to pronunciation modeling, using dynamic Bayesian networks. In this class of models, the great variety of pronunciations seen in conversational speech is explained as the result of asynchrony among feature streams and changes in individual feature values. I will also discuss the use of linguistic features in observation modeling via feature-specific classifiers. I will describe the application of these ideas in experiments with audio and visual speech recognition, and present analyses suggesting additional potential applications in speech science and technology.