Pitch has long been held as an important signalling channel when planning and deploying speech in conversation, and myriad studies have been undertaken to determine the extent to which it actually plays this role. Unfortunately, these studies have required considerable human investment in data preparation and analysis, and have therefore often been limited to a handful of specific conversational contexts. The current article proposes a framework which addresses these limitations, by enabling a scalable, quantitative characterization of the role of pitch throughout an entire conversation, requiring only the raw signal and speech activity references. The framework is evaluated on the Switchboard dialogue corpus. Experiments indicate that pitch trajectories of both parties are predictive of their incipient speech activity; that pitch should be expressed on a logarithmic scale and Z-normalized, as well as accompanied by a binary voicing variable; and that only the most recent 400 ms of the pitch trajectory are useful in incipient speech activity prediction.

The Active Listening Corpus (ALICO) is a multimodal database of spontaneous dyadic conversations with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcription with corresponding communicative function interpretation as well as segmentation of interpausal units, words, rhythmic prominence intervals and vowel-to-vowel intervals. Additionally, ALICO contains head gesture annotation of both interlocutors. The corpus contributes to research on spontaneous human–human interaction, on functional relations between modalities, and timing variability in dialogue. It also provides data that differentiates between distracted and attentive listeners. We describe the main characteristics of the corpus and present the most important results obtained from analyses in recent years.