Non-parallel voice conversion using i-vector PLDA: towards unifying speaker verification and transformation

By Tomi H. Kinnunen, Lauri Juvela, Paavo Alku, Junichi Yamagishi. 2017

72 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron (2018, methodology): “To perform voice conversion, a model must synthesize an utterance, given only the acoustic signal of that utterance in a different speaker’s voice (Wu et al., 2013; Nakashika et al., 2016; Kinnunen et al., 2017; van den Oord et al., 2017; Chorowski et al., 2017).”
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis (2018, background): “Style Transfer Style transfer is an active area of research that aims to synthesize a phrase in the prosodic style of a reference signal (Wu et al., 2013; Nakashika et al., 2016; Kinnunen et al., 2017).”
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data (2017, background): “Learning to disentangle the mixed information from the surface representation is essential for a wide variety of speech applications: for example, noise robust speech recognition [41, 38, 37, 16], speaker verification [5], and voice conversion [40, 29, 24].”
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection (2019, methodology): “They are end-to-end TTS with WaveRNN and a speaker encoder pretrained for ASV [25], VC using moment matching networks [26] and waveform filtering [12], and i-vector/PLDA based VC [27] using a DNN glottal vocoder [28], respectively.”
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning (2020, background): “[159] studies a way to shift the acoustic features of input speech towards target speech in the i-vector space.”
StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks (2018, methodology): “A method using i-vectors [30], known as a feature for speaker verification, was recently proposed in [25].”
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations (2018, background): “Examples of acoustic domains may include speaker identity [1, 2, 3, 4, 5, 6], speaking style, accent, emotion [7] or some other properties orthogonal to the linguistic content.”
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (2019, methodology): “A non-parallel VC system [44] inspired by the standard i-vector framework [45, 46] used in text-independent ASV.”
TTS Skins: Speaker Conversion via ASR (2019, methodology): “[7] have aligned the source and target GMMs, by comparing the i-vectors of the two speakers, without using transcription or parallel data.”