The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is
important to capture the diversity in human speech such as speaker identities, prosodies …

As humans, we navigate a multimodal world, building a holistic understanding from all our
senses. We introduce MERLOT Reserve, a model that represents videos jointly over time …

Generating sound effects that people want is an important topic. However, there are limited
studies in this area for sound generation. In this study, we investigate generating sound …

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 …

Recent research shows a big convergence in model architecture, training objectives, and
inference methods across various tasks for different modalities. In this paper, we propose …

In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional
and unconditional waveform generation. The model is non-autoregressive, and converts the …

This paper introduces WaveGrad, a conditional model for waveform generation which
estimates gradients of the data density. The model is built on prior work on score matching …

Text-to-speech (TTS) has made rapid progress in both academia and industry in recent
years. Some questions naturally arise that whether a TTS system can achieve human-level …