Automatic Speaker Recognition (ASR) involves a complex range of processes to extract, model, and compare speaker-specific information from a pair of voice samples.Using heavily controlled recordings, this paper explores the impact of specific vocal conditions (i.e.vocal setting, disguise, accent guises) on ASR performance.When vocal conditions are matched, ASR performance is generally excellent (whisper is an exception).When conditions are mismatched, as in most forensic cases, we see an increase in discrimination and calibration error in some cases.The most problematic mismatches are those involving whisper and supralaryngeal vocal settings; these produce the greatest phonetic changes to speech.Mismatches involving high pitch also produce poor performance, although this appears to be driven by speaker-specific differences in articulatory implementation.We discuss the implications of the findings for the use of ASR in forensic casework and the interpretability of system output.

A significant question for forensic voice comparison, and for speaker recognition more generally, is the extent to which different input features capture complementary speakerspecific information.Understanding complementarity allows us to make predictions about how combining methods using different features may produce better overall performance.In forensic contexts, it is also important to be able to explain to courts what information the underlying features are actually capturing.This paper addresses these issues by examining the extent to which MFCCs and LPCCs can predict F0, F1, F2, and F3 values using data extracted from the midpoint of the vocalic portion of the hesitation marker um for 89 speakers of standard southern British English.By-speaker correlations were calculated using multiple linear regression and performance was assessed using mean rho (𝜌) values.Results show that the first two formants were more accurately predicted than F3 or F0.LPCCs consistently produced stronger correlations with the linguistic features than MFCCs, while increasing cepstral order up to 16 also increased the strength of the correlations.There was, however, considerable variability across speakers in terms of the accuracy of the predictions.We discuss the implications of these findings for forensic voice comparison.

ISCA grants each author permission to use the paper in that author's dissertation or in institutional and public repositories

ISCA grants each author permission to use the paper in that author's dissertation or in institutional and public repositories

Many AI tasks, in particular natural language processing, require a large amount of world knowledge to create expectations, assess plausibility, and guide disambiguation. However, acquiring this world knowledge remains a formidable challenge. Building on ideas by Schubert, we have developed a system called DART (Discovery and Aggregation of Relations in Text) that extracts simple, semi-formal statements of world knowledge (e.g., "airplanes can fly", "people can drive cars") from text by abstracting from a parser's output, and we have used it to create a database of 23 million propositions of this kind. An evaluation of the DART database on two language processing tasks (parsing and textual entailment) shows that it improves performance, and a human evaluation shows that over half the facts in it are considered true or partially true, rising to 70% for facts seen with high frequency. The significance of this work is two-fold: First it has created a new, publically available knowledge resource for language processing and other data interpretation tasks, and second it provides empirical evidence of the utility of this type of knowledge, going beyond Schubert et al's earlier evaluations which were based solely on human inspection of its contents.

As part of the ongoing project, Project Halo, our goal is to build a system capable of answering questions posed by novice users to a formal knowledge base. In our current context, the knowledge base covers selected topics in physics, chemistry, and biology, and our question set consists of AP (advanced high-school) level examination questions. The task is challenging because the questions are linguistically complex and are often incomplete (assume unstated knowledge), and because the users do not have prior knowledge of the system's contents. Our solution involves two parts: a controlled language interface, in which users reformulate the original natural language questions in a simplified version of English, and a novel problem solver that can elaborate initially inadequate logical interpretations of a question by selecting relevant pieces of knowledge in the knowledge base. An evaluation of the work in 2006 showed that this approach is feasible and that complex, multisentence questions can be posed and answered, thus illustrating novel ways of dealing with the knowledge capture impedance between users and a formal knowledge base, while also revealing challenges that still remain.

We describe a method for evaluating a grammar checking application with hand-bracketed parses.A randomly-selected set of sentences was submitted to a grammar checker in both bracketed and unbracketed formats.A comparison of the resulting error reports illuminates the relationship between the underlying performance of the parsergrammar system and the error critiques presented to the user.

The artificial intelligence aspects of computer vision make LISP appropriate for some vision re search needs. At the University of Washington an experimental image understanding system has been implemented in MACLISP. Since MACLISP supports arrays and compiles into reasonably efficient code, it has been convenient for the project. The major subsystems are the following: preprocessing, region growing, feature computation, and control. A novel aspect of the system is its control structure which consists of a "task agenda" which is processed by procedures each based on heuristics relevant to image analysis. Processing one task may create other tasks and the system is designed to suggest alternatives during the course of analyzing an image. Tasks are ordered by their heuristically-computed "priority" values. The system has been applied to the problem of segmenting aerial photographs of an airport. Design issues and possible extensions for further work are discussed.