Speaker anonymization by modifying fundamental frequency and x-vector singular value. (May 2022)

• Record Type: Journal Article
• Authors: Mawalim, Candy Olivia; Galajit, Kasorn; Karnjana, Jessada; Kidani, Shunsuke; Unoki, Masashi
• Is Part Of: Computer speech & language. Volume 73(2022)
• Publication Date: 2022-05
• Subjects: Speaker anonymization -- X-vector singular value -- Fundamental frequency -- Clustering -- Subjective evaluation; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101326
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Speaker anonymization is a method of protecting voice privacy by concealing individual speaker characteristics while preserving linguistic information. The VoicePrivacy Challenge 2020 was initiated to generalize the task of speaker anonymization. In the challenge, two frameworks for speaker anonymization were introduced; in this study, we propose a method of improving the primary framework by modifying the state-of-the-art speaker individuality feature (namely, x-vector) in a neural waveform speech synthesis model. Our proposed method is constructed based on x-vector singular value modification with a clustering model. We also propose a technique of modifying the fundamental frequency and speech duration to enhance the anonymization performance. To evaluate our method, we carried out objective and subjective tests. The overall objective test results show that our proposed method improves the anonymization performance in terms of the speaker verifiability, whereas the subjective evaluation results show improvement in terms of the speaker dissimilarity. The intelligibility and naturalness of the anonymized speech with speech prosody modification were slightly reduced (less than 5% of word error rate) compared to the results obtained by the baseline system. Graphical abstract: Highlights: Anonymizing personally identifiable information is crucial to protect speech privacy. We propose an anonymization method by modifying the F 0, duration, and x-vector. A reliable subjective test is conducted to evaluate a speaker anonymization system. The results show that our proposed method improved speaker verifiability.