Studying voice quality in spontaneous conversational speech has traditionally received little attention, mostly due to difficulties inherent in isolating voice source in such variable and noisy signals. While inverse filtering methods exist, they are either extremely time consuming (when done manually) or error prone (when automatic methods are used). By contrast, in this talk I will demonstrate that some progress can be made by using acoustic features which are minimally affected by segmental structure of speech and/or by using alternative data acquisition methods. Specifically, I will present ongoing work on measuring voice quality variation using miniature accelerometers attached to speakers’ necks. These unobtrusive sensors output a signal which is robust to vocal tract influences while at the same time capturing differences between phonation types and are thus perfectly suited to investigating interactional functions of voice quality such as turn management.