Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

Voice assistants (VA) have become prevalent on a wide range of personal devices such as
smartphones and smart speakers. As companies build voice assistants with extra …

End-to-end (E2E) models, which directly predict output character sequences given input
speech, are good candidates for on-device speech recognition. E2E models, however …

This paper describes Tacotron 2, a neural network architecture for speech synthesis directly
from text. The system is composed of a recurrent sequence-to-sequence feature prediction …

A text-to-speech synthesis system typically consists of multiple stages, such as a text
analysis frontend, an acoustic model and an audio synthesis module. Building these …

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
The model is fully probabilistic and autoregressive, with the predictive distribution for each …

In this study, we propose a speaker-dependent WaveNet vocoder, a method of synthesizing
speech waveforms with WaveNet, by utilizing acoustic features from existing vocoder as …

ABSTRACT A text-to-speech synthesis system typically consists of multiple stages, such as a
text analysis frontend, an acoustic model and an audio synthesis module. Building these …

Contextual biasing to a specific domain, including a user's song names, app names and
contact names, is an important component of any production-level automatic speech …

The requirements for many applications of state-of-the-art speech recognition systems
include not only low word error rate (WER) but also low latency. Specifically, for many use …