Publications

Recent years have seen increased interest within the speaker recognition community in high-level features including, for example, lexical choice, idiomatic expressions or syntactic structures. The promise of speaker recognition in forensic applications drives development toward systems robust to channel differences by selecting features inherently robust to channel difference. Within the language recognition community, there is growing interest in differentiating not only languages but also mutually intelligible dialects of a single language. Decades of research in dialectology suggest that high-level features can enable systems to cluster speakers according to the dialects they speak. The Phanotics (Phonetic Annotation of Typicality in Conversational Speech) project seeks to identify high-level features characteristic of American dialects, annotate a corpus for these features, use the data to dialect recognition systems and also use the categorization to create better models for speaker recognition. The data, once published, should be useful to other developers of speaker and dialect recognition systems and to dialectologists and sociolinguists. We expect the methods will generalize well beyond the speakers, dialects, and languages discussed here and should, if successful, provide a model for how linguists and technology developers can collaborate in the future for the benefit of both groups and toward a deeper understanding of how languages vary and change.

This paper describes the planning and creation of the Mixer and Transcript Reading corpora, their properties and yields, and reports on the lessons learned during their development.

We describe efforts to create corpora to support and evaluate systems that meet the challenge of speaker recognition in the face of both channel and language variation. In addition to addressing ongoing evaluation of speaker recognition systems, these corpora are aimed at the bilingual and crosschannel dimensions. We report on specific data collection efforts at the Linguistic Data Consortium, the 2004 speaker recognition evaluation program organized by the National Institute of Standards and Technology (NIST), and the research ongoing at the US Federal Bureau of Investigation and MIT Lincoln Laboratory. We cover the design and requirements, the collections and evaluation integrating discussions of the data preparation, research, technology development and evaluation on a grand scale.

This paper describes efforts to create corpora to support and evaluate systems that perform speaker recognition where channel and language may vary. Beyond the ongoing evaluation of speaker recognition systems, these corpora are aimed at the bilingual and cross channel dimensions. We report on specific data collection efforts at the Linguistic Data Consortium and the research ongoing at the US Federal Bureau of Investigation and MIT Lincoln Laboratories. We cover the design and requirements, the collections and final properties of the corpus integrating discussions of the data preparation, research, technology development and evaluation on a grand scale.