Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand on the existing literature by examining bipolar disorder (BP). BP is characterized by mood transitions, varying from a healthy euthymic state to states characterized by mania or depression. The speech patterns associated with these mood states provide a unique opportunity to study the modulations characteristic of mood variation. We describe methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Our pilot investigation suggests that manic and depressive mood states can be recognized from this speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with BP.