This paper proposes a multilingual, multi-speaker (MM) TTS system by using a voice conversion (VC)-based data augmentation method. Creating an MM-TTS model is challenging, owing to the difficulties of collecting polyglot data from multiple speakers. To address this problem, we adopt a cross-lingual, multi-speaker VC model trained with multiple speakers’ monolingual databases. As this model effectively transfers acoustic attributes while retaining the content information, it is possible to generate each speaker’s polyglot corpora. Subsequently, we design the MM-TTS model with variational autoencoder (VAE)-based posterior embeddings. It is to be noted that incorporating VC-augmented polyglot corpora into the TTS training process might degrade synthetic quality, since the corpora sometimes contain unwanted artifacts. To mitigate this issue, the VAE is trained to capture the acoustic dissimilarity between the recorded and VC-augmented datasets. Through the selective choice of the posterior embeddings obtained from the original recordings in the training set, the proposed model enables the generation of acoustically clearer voices.

We propose a data cleansing method that utilizes a neural analysis and synthesis (NANSY++) framework to train an end-to-end neural diarization model (EEND) for singer diarization.Our proposed model converts song data with choral singing commonly contained in popular music and unsuitable for generating a simulated dataset to the solo singing data.This cleansing is based on NANSY++, which is a framework trained to reconstruct an input non-overlapped audio signal.We exploit the pretrained NANSY++ to convert choral singing into clean, nonoverlapped audio.This cleansing process mitigates the mislabeling of choral singing to solo singing and helps the effective training of EEND models even when the majority of available song data contains choral singing sections.We experimentally evaluated the EEND model trained with a dataset using our proposed method using annotated popular duet songs.As a result, our proposed method improved 14.8 points in diarization error rate.

Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models separately). However, they often generate unstable pitch contour with audible artifacts when the dataset contains emotional attributes, i.e., large diversity of pronunciation and prosody. To address this problem, we propose Period VITS, a novel end-to-end TTS model that incorporates an explicit periodicity generator. In the proposed method, we introduce a frame pitch predictor that predicts prosodic features, such as pitch and voicing flags, from the input text. From these features, the proposed periodicity generator produces a sample-level sinusoidal source that enables the waveform decoder to accurately reproduce the pitch. Finally, the entire model is jointly optimized in an end-to-end manner with variational inference and adversarial objectives. As a result, the decoder becomes capable of generating more stable, expressive, and natural output waveforms. The experimental results showed that the proposed model significantly outperforms baseline models in terms of naturalness, with improved pitch stability in the generated samples.

Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are available. Although the quality of VC is crucial for this approach, it is challenging to learn a stable VC model because the amount of data is limited in low-resource scenarios, and highly expressive speech has large acoustic variety. To address this issue, we propose a novel data augmentation method that combines pitch-shifting and VC techniques. Because pitch-shift data augmentation enables the coverage of a variety of pitch dynamics, it greatly stabilizes training for both VC and TTS models, even when only 1,000 utterances of the target speaker's neutral data are available. Subjective test results showed that a FastSpeech 2-based emotional TTS system with the proposed method improved naturalness and emotional similarity compared with conventional methods.