4,975 research outputs found
A Fully Time-domain Neural Model for Subband-based Speech Synthesizer
This paper introduces a deep neural network model for subband-based speech
synthesizer. The model benefits from the short bandwidth of the subband signals
to reduce the complexity of the time-domain speech generator. We employed the
multi-level wavelet analysis/synthesis to decompose/reconstruct the signal into
subbands in time domain. Inspired from the WaveNet, a convolutional neural
network (CNN) model predicts subband speech signals fully in time domain. Due
to the short bandwidth of the subbands, a simple network architecture is enough
to train the simple patterns of the subbands accurately. In the ground truth
experiments with teacher-forcing, the subband synthesizer outperforms the
fullband model significantly in terms of both subjective and objective
measures. In addition, by conditioning the model on the phoneme sequence using
a pronunciation dictionary, we have achieved the fully time-domain neural model
for subband-based text-to-speech (TTS) synthesizer, which is nearly end-to-end.
The generated speech of the subband TTS shows comparable quality as the
fullband one with a slighter network architecture for each subband.Comment: 5 pages, 3 figur
Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
This paper proposes a forward attention method for the sequenceto- sequence
acoustic modeling of speech synthesis. This method is motivated by the nature
of the monotonic alignment from phone sequences to acoustic sequences. Only the
alignment paths that satisfy the monotonic condition are taken into
consideration at each decoder timestep. The modified attention probabilities at
each timestep are computed recursively using a forward algorithm. A transition
agent for forward attention is further proposed, which helps the attention
mechanism to make decisions whether to move forward or stay at each decoder
timestep. Experimental results show that the proposed forward attention method
achieves faster convergence speed and higher stability than the baseline
attention method. Besides, the method of forward attention with transition
agent can also help improve the naturalness of synthetic speech and control the
speed of synthetic speech effectively.Comment: 5 pages, 3 figures, 2 tables. Published in IEEE International
Conference on Acoustics, Speech and Signal Processing 2018 (ICASSP2018
Sampling-based speech parameter generation using moment-matching networks
This paper presents sampling-based speech parameter generation using
moment-matching networks for Deep Neural Network (DNN)-based speech synthesis.
Although people never produce exactly the same speech even if we try to express
the same linguistic and para-linguistic information, typical statistical speech
synthesis produces completely the same speech, i.e., there is no
inter-utterance variation in synthetic speech. To give synthetic speech natural
inter-utterance variation, this paper builds DNN acoustic models that make it
possible to randomly sample speech parameters. The DNNs are trained so that
they make the moments of generated speech parameters close to those of natural
speech parameters. Since the variation of speech parameters is compressed into
a low-dimensional simple prior noise vector, our algorithm has lower
computation cost than direct sampling of speech parameters. As the first step
towards generating synthetic speech that has natural inter-utterance variation,
this paper investigates whether or not the proposed sampling-based generation
deteriorates synthetic speech quality. In evaluation, we compare speech quality
of conventional maximum likelihood-based generation and proposed sampling-based
generation. The result demonstrates the proposed generation causes no
degradation in speech quality.Comment: Submitted to INTERSPEECH 201
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