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Brochure 2020
Discriminative Autoencoder-based Speaker/Speech Figure 6 : Discriminative Autoencoder-based Speaker/Speech
Recognition Recognition. Speaker-related information is extracted
from speaker-independent factors with di erent loss
Autoencoders are often used to efficiently encode in functions at the code layer, so that the speaker-related
unsupervised learning. We rst proposed a discriminative representation has better discriminating power and
autoencoder (DcAE) in 2017, which has been applied recognition accuracy is improved.
to speaker recognition. The basis of this research is to
separate speaker-related information from speaker-
independent factors by considering different loss
functions at the code layer, so that speaker-related
representations are better discriminated, thereby
improving recognition accuracy. In 2019, we modified
the structure of our DcAE and successfully applied it to
TDNN and TDNN-LSTM acoustic model architectures in
the nnet3 setup of the Kaldi speech recognition toolkit.
The corresponding recipe for the WSJ corpus was
released, and the results of this work were published in
Interspeech2019.
Variational Autoencoder-based Voice Conversion two kinds of spectral features, further improving the quality
of the converted speech and the similarity to the target
Voice conversion (VC) aims to convert the speech of a speaker. That advance was published in ISCSLP2018, and
source speaker to that of a target speaker without changing the paper won a Best Student Paper Award. In 2019, we
the linguistic content. In 2016, we rst applied a variational introduced the WaveNet vocoder into our VAE-based VC
autoencoder (VAE) to voice conversion under non-parallel system to replace the traditional WORLD vocoder. We also
training conditions. The resulting paper was published in proposed a method to remove fundamental frequency
APSIPA ASC 2016, which has been cited 102 times to date (F0) information at the code layer. A respective paper
(Google Scholar data). In 2017, we further integrated a integrating those research results has recently been
generative adversarial network (GAN) into our VAE-based accepted for publication by IEEE Trans. on ETCI.
VC system. That work was published in Interspeech2017
(cited 145 times, Google Scholar data). Then, in 2018, we
proposed a cross-domain VAE that simultaneously models
Figure 7 : Voice conversion (VC) Diagram. Our study focus on the modeling and learning of the spectral
feature encoder (Eθ) and decoder (GΦ).
Future Topics /
We are continuing to seek more favorable approaches for conducting continuous lifelong learning and
integrating them into various applications. We have recently begun cooperating with NTU Hospital to identify
early and appropriate applications of smart emergency medicine, and are planning to collaborate with Microsoft
Newsgroup on re ning the editing process.
45
Discriminative Autoencoder-based Speaker/Speech Figure 6 : Discriminative Autoencoder-based Speaker/Speech
Recognition Recognition. Speaker-related information is extracted
from speaker-independent factors with di erent loss
Autoencoders are often used to efficiently encode in functions at the code layer, so that the speaker-related
unsupervised learning. We rst proposed a discriminative representation has better discriminating power and
autoencoder (DcAE) in 2017, which has been applied recognition accuracy is improved.
to speaker recognition. The basis of this research is to
separate speaker-related information from speaker-
independent factors by considering different loss
functions at the code layer, so that speaker-related
representations are better discriminated, thereby
improving recognition accuracy. In 2019, we modified
the structure of our DcAE and successfully applied it to
TDNN and TDNN-LSTM acoustic model architectures in
the nnet3 setup of the Kaldi speech recognition toolkit.
The corresponding recipe for the WSJ corpus was
released, and the results of this work were published in
Interspeech2019.
Variational Autoencoder-based Voice Conversion two kinds of spectral features, further improving the quality
of the converted speech and the similarity to the target
Voice conversion (VC) aims to convert the speech of a speaker. That advance was published in ISCSLP2018, and
source speaker to that of a target speaker without changing the paper won a Best Student Paper Award. In 2019, we
the linguistic content. In 2016, we rst applied a variational introduced the WaveNet vocoder into our VAE-based VC
autoencoder (VAE) to voice conversion under non-parallel system to replace the traditional WORLD vocoder. We also
training conditions. The resulting paper was published in proposed a method to remove fundamental frequency
APSIPA ASC 2016, which has been cited 102 times to date (F0) information at the code layer. A respective paper
(Google Scholar data). In 2017, we further integrated a integrating those research results has recently been
generative adversarial network (GAN) into our VAE-based accepted for publication by IEEE Trans. on ETCI.
VC system. That work was published in Interspeech2017
(cited 145 times, Google Scholar data). Then, in 2018, we
proposed a cross-domain VAE that simultaneously models
Figure 7 : Voice conversion (VC) Diagram. Our study focus on the modeling and learning of the spectral
feature encoder (Eθ) and decoder (GΦ).
Future Topics /
We are continuing to seek more favorable approaches for conducting continuous lifelong learning and
integrating them into various applications. We have recently begun cooperating with NTU Hospital to identify
early and appropriate applications of smart emergency medicine, and are planning to collaborate with Microsoft
Newsgroup on re ning the editing process.
45
