Calculating speaker pitch (or f0) is typically the first computational step in modeling tone and intonation for spoken language understanding. Usually pitch is treated as a fixed, single-valued quantity. The inherent ambiguity judging the octave of pitch, as well as spurious values, leads to errors in modeling pitch gestures that propagate in a computational pipeline. We present an alternative that instead measures changes in the harmonic structure using a subband autocorrelation change detector (SACD). This approach builds upon new machine-learning ideas for how to integrate autocorrelation information across subbands. Importantly however, for modeling gestures, we preserve multiple hypotheses and integrate information from all harmonics over time. The benefits of SACD over standard pitch approaches include robustness to noise and amount of voicing. This is important for real-world data in terms of both acoustic conditions and speaking style. We discuss applications in tone and intonation modeling, and demonstrate the efficacy of the approach in a Mandarin Chinese tone-classification experiment. Results suggest that SACD could replace conventional pitch-based methods for modeling gestures in selected spoken-language processing tasks.