Neural codecs have become crucial to recent speech and audio generation research. In
addition to signal compression capabilities, discrete codecs have also been found to …

Preference tuning is a crucial process for aligning deep generative models with human
preferences. This survey offers a thorough overview of recent advancements in preference …

The last decade has witnessed significant advancements in deep learning-based speech
enhancement (SE). However, most existing SE research has limitations on the coverage of …

Subjective speech quality assessment (SSQA) is critical for evaluating speech samples as
perceived by human listeners. While model-based SSQA has enjoyed great success thanks …

In this work, we introduce VERSA, a unified and standardized evaluation toolkit designed for
various speech, audio, and music signals. The toolkit features a Pythonic interface with …

Speech quality assessment typically requires evaluating audio from multiple aspects, such
as mean opinion score (MOS) and speaker similarity (SIM) etc., which can be challenging to …

We propose a massively multilingual speech-to-text neural forced aligner that supports 98
languages with a single architecture. The aligner takes self-supervised discrete acoustic …

The amount of articulatory data available for training deep learning models is much less
compared to acoustic speech data. In order to improve articulatory-to-acoustic synthesis …

We introduce AnyEnhance, a unified generative model for voice enhancement that
processes both speech and singing voices. Based on a masked generative model …

This paper introduces BnTTS (Bangla Text-To-Speech), the first framework for Bangla
speaker adaptation-based TTS, designed to bridge the gap in Bangla speech synthesis …