Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …

We propose a novel training strategy for Tacotron-based text-to-speech (TTS) system that
improves the speech styling at utterance level. One of the key challenges in prosody …

While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods
in many ways, the exposure bias problem in the autoregressive models remains an issue to …

This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice
conversion challenge (VCC) 2020. We consider a naive approach for voice conversion (VC) …

Tacotron-based end-to-end speech synthesis has shown remarkable voice quality.
However, the rendering of prosody in the synthesized speech remains to be improved …

Recent advances in neural TTS have led to models that can produce high-quality synthetic
speech. However, these models typically require large amounts of training data, which can …

Deepfakes, particularly in the auditory domain, have become a significant threat,
necessitating the development of robust countermeasures. This paper addresses the …

The Armenian language is an independent branch of the Indo-European language family
and the official language of the Republic of Armenia and the Republic of Artsakh. According …

This paper proposes a novel speech-rhythm-based method for speaker embeddings.
Conventionally spectral feature-based speaker embedding vectors such as the x-vector are …

This paper proposes deep Gaussian process (DGP)-based frameworks for multi-speaker
speech synthesis and speaker representation learning. A DGP has a deep architecture of …