Asset Details
Do you wish to reserve the book?
Data strategies in forensic automatic speaker comparison
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Data strategies in forensic automatic speaker comparison
Do you wish to request the book?
Data strategies in forensic automatic speaker comparison
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Data strategies in forensic automatic speaker comparison
2023
Overview
Automatic speaker recognition (ASR) is a method used in forensic speaker comparison (FSC) casework. It needs collections of audio data that are representative of the case audio in order to perform reference normalization and to train a score-to-LR function. Audio from a certain minimum number of speakers is needed for each of those purposes to obtain relatively stable performance of ASR. Although it is not possible to set a hard cut-off, for the purpose of this work this number was chosen to be 30 for each, and 60 for both. Lack of representative data from that many speakers and uncertainty about what exactly constitutes representative data are major reasons for not employing ASR in FSC.
An experiment was carried out in which a situation was simulated where a practitioner has only 30 speakers available. Several data strategies are tried out to handle the lack of data: leaving out reference normalization, splitting the 30 speakers into two groups of 15 (ignoring the minimum of 30) and a leave 1 or 2 out strategy where all 30 speakers are used for both reference normalization and calibration. They are compared to the baseline situation where the practitioner does have the required 60 speakers. The leave 1 or 2 out strategy with 30 speakers performs on par with baseline, and extension of that strategy to the full 60 speakers even outperforms baseline. This shows that a strategy that halves the data need is viable, lessening the data requirements for ASR in FSC and making the use of ASR possible in more cases.
•Several data strategies for forensic speaker comparison casework are compared.•When having too few data, one strategy is found that still performs on par with having enough data.•Data need for casework is cut in half.•When there is enough data, a data strategy is found that outperforms a baseline strategy.
Publisher
Elsevier B.V,Elsevier Limited
