1,337 research outputs found
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
model. We learn to listen and write characters with a joint Connectionist
Temporal Classification (CTC) and attention-based encoder-decoder network. The
encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
The CTC network sits on top of the encoder and is jointly trained with the
attention-based decoder. During the beam search process, we combine the CTC
predictions, the attention-based decoder predictions and a separately trained
LSTM language model. We achieve a 5-10\% error reduction compared to prior
systems on spontaneous Japanese and Chinese speech, and our end-to-end model
beats out traditional hybrid ASR systems.Comment: Accepted for INTERSPEECH 201
LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices
Recent developments in speech synthesis have produced systems capable of
outcome intelligible speech, but now researchers strive to create models that
more accurately mimic human voices. One such development is the incorporation
of multiple linguistic styles in various languages and accents.
HMM-based Speech Synthesis is of great interest to many researchers, due to
its ability to produce sophisticated features with small footprint. Despite
such progress, its quality has not yet reached the level of the predominant
unit-selection approaches that choose and concatenate recordings of real
speech. Recent efforts have been made in the direction of improving these
systems.
In this paper we present the application of Long-Short Term Memory Deep
Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order
to obtain closer spectral characteristics to those of natural speech. The
results show how HMM-voices could be improved using this approach.Comment: 5 pages, 5 figure
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