397 citations · 712 across the 5 of their papers we have counts for
5 papers
VocBench: A Neural Vocoder Benchmark for Speech Synthesis
Ehab A. AlBadawy, Andrew Gibiansky, Qing He +3
Neural vocoders, used for converting the spectral representations of an audio signal to the waveforms, are a commonly used component in speech synthesis pipelines. It focuses on sy…
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Wei Ping, Kainan Peng, Andrew Gibiansky +5
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturaln…
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Sercan Arik, Gregory Diamos, Andrew Gibiansky +5
We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting…
Deep Voice: Real-time Neural Text-to-Speech
Sercan O. Arik, Mike Chrzanowski, Adam Coates +9
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech…
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Sercan O. Arik, Markus Kliegl, Rewon Child +5
Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprin…