Speech datasets from many languages, styles, and sources exist in the world, representing significant potential for scientific studies of speech—particularly given structural similarities among all speech datasets. However, studies using multiple speech corpora remain difficult in practice, due to corpus size, complexity, and differing formats. We introduce open-source software for unified corpus analysis: integrating speech corpora and querying across them. Corpora are stored in a custom ‘polyglot persistence’scheme that combines three sub-databases mirroring different data types: a Neo4j graph database to represent temporal annotation graph structure, and SQL and InfluxDB databases to represent meta-and acoustic data. This scheme abstracts away from the idiosyncratic formats of different speech corpora, while mirroring the structure of different data types improves speed and scalability. A Python API and a GUI both allow for: enriching the database with positional, hierarchical, temporal, and signal measures (eg utterance boundaries, f0) that are useful for linguistic analysis; querying the database using a simple query language; and exporting query results to standard formats for further analysis. We describe the software, summarize two case studies using it to examine effects on pitch and duration across languages, and outline planned future development.