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Improving Security in McAdams Coefficient-Based Speaker Anonymization by Watermarking Method

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Candy Olivia Mawalim, Masashi Unoki

Cryptography and Security · Submitted 15 Jul 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We conducted objective evaluations with reference to the VoicePrivacy 2020 Challenge (VP2020) and of the speech watermarking with reference to the Information Hiding Challenge (IHC) and found that our method could satisfy the blind detection, inaudibility, and robustness requirements in watermarking.

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arXiv ID2107.07223 (version 1)
Pith IDA76JSYKL, Pith's permanent record of this paper and its review
CitationCandy Olivia Mawalim, Masashi Unoki. "Improving Security in McAdams Coefficient-Based Speaker Anonymization by Watermarking Method." arXiv:2107.07223 (2021). https://pith.science/paper/A76JSYKL
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,850 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.20 · controlled comparison 0.92 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.97 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Speaker anonymization aims to suppress speaker individuality to protect privacy in speech while preserving the other aspects, such as speech content. One effective solution for anonymization is to modify the McAdams coefficient. In this work, we propose a method to improve the security for speaker anonymization based on the McAdams coefficient by using a speech watermarking approach. The proposed method consists of two main processes: one for embedding and one for detection. In embedding process, two different McAdams coefficients represent binary bits ``0" and ``1". The watermarked speech is then obtained by frame-by-frame bit inverse switching. Subsequently, the detection process is carried out by a power spectrum comparison. We conducted objective evaluations with reference to the VoicePrivacy 2020 Challenge (VP2020) and of the speech watermarking with reference to the Information Hiding Challenge (IHC) and found that our method could satisfy the blind detection, inaudibility, and robustness requirements in watermarking. It also significantly improved the anonymization performance in comparison to the secondary baseline system in VP2020.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Speaker anonymization aims to suppress speaker individuality to protect privacy in speech while preserving the other aspects, such as speech content. background 1.00 0.04 0.02 asserted 1.00
s1 One effective solution for anonymization is to modify the McAdams coefficient. background 1.00 0.06 0.03 asserted 0.96
s2 In this work, we propose a method to improve the security for speaker anonymization based on the McAdams coefficient by using a speech watermarking approach. contribution 1.00 0.73 0.14 asserted 0.95
s3 The proposed method consists of two main processes: one for embedding and one for detection. method 0.96 0.65 0.03 asserted 0.99
s4 In embedding process, two different McAdams coefficients represent binary bits ``0" and ``1". method 1.00 0.47 0.02 asserted 1.00
s5 The watermarked speech is then obtained by frame-by-frame bit inverse switching. method 1.00 0.68 0.03 asserted 1.00
s6 Subsequently, the detection process is carried out by a power spectrum comparison. method 1.00 0.69 0.03 asserted 0.98
s7 We conducted objective evaluations with reference to the VoicePrivacy 2020 Challenge (VP2020) and of the speech watermarking with reference to the Information Hiding Challenge (IHC) and found that our method could satisfy the blind detection, inaudibility, and robustness requirements in watermarking. result 0.99 0.97 0.54 hedged possibility 0.77
s8 It also significantly improved the anonymization performance in comparison to the secondary baseline system in VP2020. result 1.00 0.90 0.42 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.93.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Speaker anonymization aims to suppress speaker individuality to protect privacy in speech while preserving the other aspects, such as speech content. One effective solution for anonymization is to modify the McAdams coefficient. In this work, we propose a method to improve the security for speaker anonymization based on the McAdams coefficient by using a speech watermarking approach. The proposed method consists of two main processes: one for embedding and one for detection. In embedding process, two different McAdams coefficients represent binary bits ``0" and ``1". The watermarked speech is then obtained by frame-by-frame bit inverse switching. Subsequently, the detection process is carried out by a power spectrum comparison. We conducted objective evaluations with reference to the VoicePrivacy 2020 Challenge (VP2020) and of the speech watermarking with reference to the Information Hiding Challenge (IHC) and found that our method could satisfy the blind detection, inaudibility, and robustness requirements in watermarking. It also significantly improved the anonymization performance in comparison to the secondary baseline system in VP2020.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Cites as background 1 paper
  • In this challenge, an anonymization system suppressed PII while maintaining the intelligibility and naturalness of a speech signal [ref] .Two baseline systems were provided: (1) a primary baseline based on a state-of-the-art x-vector embedding and neural waveform modelling [ref] , and (2) a secondary baseline based on the manipulation of pole locations derived by linear predictive coding (LPC) using the McAdams coefficient [ref] .

    Speaker anonymisation using the McAdams coefficient · Introduction · 0.93

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