Index Terms: Speech denoising, speech enhancement, deep learning, context aggregation network, deep feature loss 1. Introduction Speech denoising (or enhancement) refers to the removal of background content from speech signals . Due to the ubiq-uity of this audio degradation, denoising has a key role in im-
Building Mongolian TTS Front-End with Encoder-Decoder Model by Using Bridge Method and Multi-view Features Rui Liu, Feilong Bao, and Guanglai Gao. (ICONIP 2019) Mongolian Text-to-Speech System Based on Deep Neural Network Rui Liu, Feilong Bao, Guanglai Gao and Yonghe Wang. (NCMMSC 2017, Oral).
sequences given acoustic features as input. This is a direct, discriminative approach to building a speech recognition system in contrast to the gen-erative, noisy-channel approach which motivates HMM-based speech recognition systems. Our ap-plication of the CTC loss function follows the ap-proach introduced by Graves and Jaitly (2014), but
Speech Denoising with Deep Feature Losses. Contribute to francoisgermain/SpeechDenoisingWithDeepFeatureLosses development by creating an account on GitHub.
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3 TEXT TO SPEECH SYNTHESIS (TTS) 0 0.5 1 1.5 2 2.5 3 3.5 USD Billions Global TTS Market Value 1 2016 2022 Apple Siri Microsoft Cortana Amazon Alexa / Polly Nuance
Chao Zhang Chao Zhang 0001 Peking University, Key Laboratory of Machine Perception, Beijing, China Chao Zhang 0002 University of Wollongong, School of Engineering Physics, NSW, Au
We trained and evaluated the Deep Learning model on NSDTSEA and VOiCES (created by Lab41) datasets. The model is based on the architecture introduced by François et. al. in "Speech Denoising with Deep Feature Losses". We use a DragonBoard 410C to capture audio in real-time, denoise and play back the noise-free speech. Challenges we ran into