• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.35-40
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• 2015

We propose a speech waveform forgery detection algorithm based on the flatness of frequency distribution. We devise a new measure of flatness which emphasizes the local change of the frequency distribution. Our measure calculates the sum of the differences between the energies of neighboring frequency bands. We compare the proposed measure with conventional flatness measures using a set of a large amount of test sounds. We also compare- the proposed method with conventional detection algorithms based on spectral distances. The results show that the proposed method gives lower equal error rate for the test set compared to the conventional methods.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.103-112
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• 2022

Due to the popularization of smartphones, most of the recorded speech files submitted as evidence of recent crimes are produced by smartphones, and the integrity (forgery) of the submitted speech files based on smartphones is emerging as a major issue in the investigation and trial process. Samsung smartphones with the highest domestic market share are distributed with built-in speech recording applications that can record calls and voice, and can edit recorded speech. Unlike editing through third-party speech (audio) applications, editing by their own builtin speech applications has a high similarity to the original file in metadata structures and attributes, so more precise analysis techniques need to prove integrity. In this study, we constructed a speech file metadata database for speech files (original files) recorded by 34 Samsung smartphones and edited speech files edited by their built-in speech recording applications. We analyzed by comparing the metadata structures and attributes of the original files to their edited ones. As a result, we found significant metadata differences between the original speech files and the edited ones.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.21-28
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• 2014

We propose a novel scheme for digital audio authentication of given audio files which are edited by inserting small audio segments from different environmental sources. The purpose of this research is to detect inserted sections from given audio files. We expect that the proposed method will assist human investigators by notifying suspected audio section which considered to be recorded or transmitted on different environments. GMM-UBM and GSV-SVM are applied for modeling the dominant environment of a given audio file. Four kinds of likelihood ratio based scores and SVM score are used to measure the likelihood for a dominant environment model. We also use an ensemble score which is a combination of the aforementioned five kinds of scores. In the experimental results, the proposed method shows the lowest average equal error rate when we use the ensemble score. Even when dominant environments were unknown, the proposed method gives a similar accuracy.