Text-to-speech systems (TTS) have come a long way in the last decade and are now a
popular research topic for creating various human-computer interaction systems. Although, a …

Purpose: As artificial intelligence (AI) takes an increasingly prominent role in health care, a
growing body of research is being dedicated to its application in the investigation of …

The amount of labeled data to train models for speech tasks is limited for most languages,
however, the data scarcity is exacerbated for speech translation which requires labeled data …

Self-supervised learning (SSL) based speech foundation models have been applied to a
wide range of ASR tasks. However, their application to dysarthric and elderly speech via …

While neural text-to-speech (TTS) has achieved human-like natural synthetic speech,
multilingual TTS systems are limited to resource-rich languages due to the need for paired …

In this paper, we propose a novel unsupervised text-to-speech acoustic model training
scheme, named UTTS, which does not require text-audio pairs. UTTS is a multi-speaker …

Automatic speech recognition (ASR) has ensured a convenient and fast mode of
communication between humans and computers. It has become more accurate over the …

Although humans engaged in face-to-face conversation simultaneously communicate both
verbally and non-verbally methods for joint and unified synthesis of speech audio and co …

Though neural text-to-speech (TTS) models show remarkable performance, they still require
a large amount of paired dataset, which is expensive to collect. The heavy demand for …

In this paper, we propose a novel unsupervised text-to-speech (UTTS) framework which
does not require text-audio pairs for the TTS acoustic modeling (AM). UTTS is a multi …