Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings

09/26/2022
by   Dávid Sztahó, et al.
0

In forensic voice comparison the speaker embedding has become widely popular in the last 10 years. Most of the pretrained speaker embeddings are trained on English corpora, because it is easily accessible. Thus, language dependency can be an important factor in automatic forensic voice comparison, especially when the target language is linguistically very different. There are numerous commercial systems available, but their models are mainly trained on a different language (mostly English) than the target language. In the case of a low-resource language, developing a corpus for forensic purposes containing enough speakers to train deep learning models is costly. This study aims to investigate whether a model pre-trained on English corpus can be used on a target low-resource language (here, Hungarian), different from the model is trained on. Also, often multiple samples are not available from the offender (unknown speaker). Therefore, samples are compared pairwise with and without speaker enrollment for suspect (known) speakers. Two corpora are applied that were developed especially for forensic purposes, and a third that is meant for traditional speaker verification. Two deep learning based speaker embedding vector extraction methods are used: the x-vector and ECAPA-TDNN. Speaker verification was evaluated in the likelihood-ratio framework. A comparison is made between the language combinations (modeling, LR calibration, evaluation). The results were evaluated by minCllr and EER metrics. It was found that the model pre-trained on a different language but on a corpus with a huge amount of speakers performs well on samples with language mismatch. The effect of sample durations and speaking styles were also examined. It was found that the longer the duration of the sample in question the better the performance is. Also, there is no real difference if various speaking styles are applied.

READ FULL TEXT

page 9

page 13

page 14

research
06/13/2023

Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech

In this paper, we study the impact of the ageing on modern deep speaker ...
research
02/27/2023

Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech

Pause insertion, also known as phrase break prediction and phrasing, is ...
research
04/10/2020

Generating Multilingual Voices Using Speaker Space Translation Based on Bilingual Speaker Data

We present progress towards bilingual Text-to-Speech which is able to tr...
research
11/04/2019

Voice Biometrics Security: Extrapolating False Alarm Rate via Hierarchical Bayesian Modeling of Speaker Verification Scores

How secure automatic speaker verification (ASV) technology is? More conc...
research
12/31/2019

Statistical Models in Forensic Voice Comparison

This chapter describes a number of signal-processing and statistical-mod...
research
01/14/2019

Exploring Transfer Learning for Low Resource Emotional TTS

During the last few years, spoken language technologies have known a big...
research
04/27/2022

Study on the Fairness of Speaker Verification Systems on Underrepresented Accents in English

Speaker verification (SV) systems are currently being used to make sensi...

Please sign up or login with your details

Forgot password? Click here to reset