Editing State-of-the-art (section)

==== Jin et al. (2023): Voice-preserving zero-shot multiple accent conversion ====
'''Summary:''' Separating accent from speaker identity is usually the hardest, because each speaker in the dataset has one single accent. Previous attempts at doing this include:

* use adversarial learning to get a discriminator to wipe out speaker-dependent information from content embeddings.
* quantization of different features in speech to obscure undesired information.

The main problem with conventional approaches to conversion is that they very often require available utterances with the same text in both source and target accent, making their applicability very limited. Alternatively, different approaches require either training or fine-tuning on the input utterances.

The current paper uses a pronunciation encoder, an acoustic encoder, and a HiFiGAN voice decoder. During training, the model minimises reconstruction loss between input and output mel-spectrograms. The pronunciation encoder synthesizes accent-dependent pronunciation sequences using accent IDs. The acoustic encoder mapss MFCCs and periodicity features to a single vector, while adversarial training removes accent information. Lastly, the decoder reconstructs waveforms from the processed features. The model is evaluated on audio quality, speaker similarity, and accent conversion effectiveness.

'''Results:''' Results indicate it maintains comparable audio quality to the original, maintains speaker similarity, and is efficient in replicating perceived nativeneess. However, listeners struggled to identify synthesized accents if they were unfamiliar with the target language (e.g. a native US listener could not classify a Korean accent on English as such, but a bilingual Korean-American listener could). Overall, the paper presents one of the best performing ACMs, that is able to preserve both speaker identity and acoustic quality during conversion. 

'''Critical observations:''' I think this paper achives a lot given that it's zero shot, but I am a bit critical about just how 'zero-shot' it truly is. They use a pre-trained acoustic model and while they do not require accent labels or speaker IDs, it seems that their training set contains over 24h of accented speech for all accents that they're synthesizing in. Additionally, none of their code is openly available, which is understandable for a private corporation like Meta, but it's still a bit disappointing.