4,277 research outputs found
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
Towards Personalized Synthesized Voices for Individuals with Vocal Disabilities: Voice Banking and Reconstruction
When individuals lose the ability to produce their own speech, due to degenerative diseases such as motor neurone disease (MND) or Parkinson’s, they lose not only a functional means of communication but also a display of their individual and group identity. In order to build personalized synthetic voices, attempts have been made to capture the voice before it is lost, using a process known as voice banking. But, for some patients, the speech deterioration frequently coincides or quickly follows diagnosis. Using HMM-based speech synthesis, it is now possible to build personalized synthetic voices with minimal data recordings and even disordered speech. The power of this approach is that it is possible to use the patient’s recordings to adapt existing voice models pre-trained on many speakers. When the speech has begun to deteriorate, the adapted voice model can be further modified in order to compensate for the disordered characteristics found in the patient’s speech. The University of Edinburgh has initiated a project for voice banking and reconstruction based on this speech synthesis technology. At the current stage of the project, more than fifteen patients with MND have already been recorded and five of them have been delivered a reconstructed voice. In this paper, we present an overview of the project as well as subjective assessments of the reconstructed voices and feedback from patients and their families
Robust Speaker-Adaptive HMM-based Text-to-Speech Synthesis
This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ``HTS-2007,'' employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
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 network that maps character embeddings
to mel-scale spectrograms, followed by a modified WaveNet model acting as a
vocoder to synthesize timedomain waveforms from those spectrograms. Our model
achieves a mean opinion score (MOS) of comparable to a MOS of for
professionally recorded speech. To validate our design choices, we present
ablation studies of key components of our system and evaluate the impact of
using mel spectrograms as the input to WaveNet instead of linguistic, duration,
and features. We further demonstrate that using a compact acoustic
intermediate representation enables significant simplification of the WaveNet
architecture.Comment: Accepted to ICASSP 201
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