̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶
第126回 音声言語情報処理研究会 (SIG-SLP) http://sig-slp.jp/̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶̶
日程：2019/02/27(水)~28(木)会場：舌切雀のお宿 ホテル磯部ガーデン（磯部温泉）
4F 桜の間 (411)〒379-0127群馬県安中市磯部1-12-5【TEL】027-385-0085【FAX】027-385-0055
URL http://www.isobesuzume.co.jp
会場への交通アクセス：

東京→上越新幹線（50分）→ 高崎→ 信越本線（18分）→磯部→徒歩（5分）→磯部ガーデン

Technical Program
02/27

1日目

02/28

2日目

受付開始

7:00〜

朝食

桜の間 411)9:00

13:00~

一般講演１（英語）

10:00-

一般講演２ (日本語)

14:15(座長：山岸順一)

10:50(座長：篠崎隆宏)

12:00~

14:3015:30

15:4016:40

16:5017:50

(4F

招待講演１
Prof. Zhenhua Ling
(座長：Xin Wang)
招待講演２
Dr. Wei Ping
(座長：Xin Wang)
招待講演３
Dr. Kong Aik Lee(座長：Xin Wang)

11:00~
12:00

(2F

鳳凰の間)

【企画】
Interspeech2018
報告会

12:00-

昼食

13:00（4F 桜の間 412）

13:0014:00

招待講演４
大木 哲史 博士
(座長：塩田さやか)
SIG-SLP企業賞発表と紹介

18:3020:00

夕食（会場：3F竹の間）

14:00-

Fairly Devices賞

14:30

Yahoo! JAPAN賞(座長：西村 雅史)

21:0022:00【企画】
国際学会既発表セッション(座長：高木信二)

15:00

富岡製糸場見学（希望者のみ、私費）都合により、アナウンスなくスケジュールが変更になることがあります。

招待講演1

Prof. Zhenhua Ling
University of Science and Technology of China, China
Deep Learning-Based Voice Conversion
Abstract:
I will introduce our recent work on applying deep learningtechniques to voice conversion in this talk. Several methods havebeen proposed to improve different components in the pipeline ofa statistical parametric voice conversion system, including deepneural networks with layer-wise generative training for acoustic
modeling, deep autoencoders with binary distributed hidden units
for feature representation, and WaveNet vocoder with limited
training data for waveform reconstruction. Then, I will introduce
our system designed for Voice Conversion Challenge 2018,
which achieved the best performance under both parallel andnon-parallel conditions in this evaluation. After this, I will present
our recent progress on sequence-to-sequence acoustic modelingfor voice conversion, which converts the acoustic features anddurations of source utterances simultaneously using a unified
acoustic model. Finally, some discussions on the future
development of voice conversion techniques will be given.
Bio:
Zhenhua Ling received the B.E. degree in electronic informationengineering, the M.S. and Ph.D. degree in signal and information
processing from the University of Science and Technology ofChina, Hefei, China, in 2002, 2005, and 2008, respectively. From
October 2007 to March 2008, he was a Marie Curie Fellow with
the Centre for Speech Technology Research, University of
Edinburgh, Edinburgh, U.K. From July 2008 to February 2011, he
was a joint Postdoctoral Researcher with the University of
Science and Technology of China, Hefei, China, and iFLYTEKCo., Ltd., China. He is currently an Associate Professor with the
University of Science and Technology of China. He also worked
at the University of Washington, Seattle, WA, USA, as a Visiting
Scholar from August 2012 to August 2013. His research interests
include speech processing, speech synthesis, voice conversion,and natural language processing. He was the recipient of theIEEE Signal Processing Society Young Author Best Paper Awardin 2010. He is now an Associate Editor for the IEEE/ACM
transactions on audio, speech, and language processing.

招待講演2

Dr. Wei Ping
Baidu Research, USA
End-to-End Neural TTS and Parallel Wave Generation
Abstract:
There are two phenomenal trends in speech synthesis research:
1) directly generating waveform samples through state-of-the-art
generative models, and 2) building end-to-end TTS systems
without too much expert knowledge. In this talk, I will first present
our recent work on parallel wave generation, which removes theautoregressive inference bottleneck in WaveNet. I will also
compare various non-autoregressive generative models forwaveform synthesis. In addition, “end-to-end"speech synthesis
actually refers to the text-to-spectrogram models with a separate
vocoder in previous studies. I will introduce the first text-to-wave
neural architecture for TTS, which is fully convolutional andenables truly end-to-end training from scratch.
Bio:
Wei Ping obtained his Ph.D. in Computer Science from
University of California, Irvine (UCI) in 2016. Before that, hereceived his B.E. and M.E. degrees from Harbin Institute ofTechnology and Tsinghua University in 2008 and 2011,
respectively. He is currently a Senior Research Scientist at Baidu
Research (USA), leading their team on speech synthesis. His
research area is machine learning and speech synthesis, withinterests spread over deep learning, generative models,graphical models and variational inference.
He has published a series of advanced deep learning papers on
text-to-speech synthesis, including Deep Voice 2 (NIPS’17),
Deep Voice 3 (ICLR’18), Neural Voice Cloning (NIPS’18) and
ClariNet (Submitted to ICLR 2019), which are well-known amongspeech synthesis researchers.

招待講演３

Dr. Kong Aik Lee
NEC Corp, Central Research Laboratories, Japan
Speaker Embedding and Recent Advances in Speaker
Recognition Evaluation
Abstract:
Automatic speaker recognition is the task of identifying or verifying
an individual’s identity from their voice samples using machinelearning algorithms, without any human intervention. Voice is acombination of physical and behavioral biometrics characteristics.
The physical features of an individual’s voice are based on the
shape and size of the vocal tract, mouth, nasal cavities, and lipsthat involve in producing speech sound. The behavioral aspects,
which include the use of a particular accent, intonation style,pronunciation pattern, choice of vocabulary and so on, areassociated more with the words or lexical content of the spoken
utterances. Speaker recognition has seen significant advancements
over the past few decades, giving rise to the successful introductionof commercial products. With the advent of Big Data and theresurrection of data-hungry modeling techniques such as artificial
neural networks, more recently the research focus has shifted froma more controlled scenario towards larger and more realistic
speaker in the wild scenarios. The latest cycle of NIST evaluations(SRE’18), which in addition to traditional conversational telephonyspeech (CTS) involves voice over IP (VOIP) data as well as audio
extracted from online videos. This talk aim to present recent
technical advances in speaker recognition and NIST SRE’18 fromNEC perspective.
Bio:
Kong Aik Lee is a Senior Principal Researcher, Biometrics
Research Laboratories, NEC Corp., Japan. He received his B.E.
(First Class Honours) in Electrical Engineering from University
Technology Malaysia (UTM), Malaysia in 1999 and Ph.D. from
Nanyang Technological University (NTU), Singapore in 2005. From2006 to 2018, He was a research scientist at the Human Language
Technology Department, Institute for Infocomm Research (I2R),A*STAR, Singapore. His current research interests include speakerrecognition and characterization, multilingual recognition andidentification, speech analysis and processing, machine learningand digital signal processing. He also serves as an Editorial Board
Member for Elsevier Computer Speech and Language, andAssociate Editor for IEEE/ACM Transactions on Audio, Speech andLanguage Processing. He is a senior member of IEEE.

招待講演4

大木 哲史 博士
静岡大

生体認証に対するなりすまし攻撃とその対策

概要:
機械学習アルゴリズムに関する研究の発展は目覚ましいが，
近年ではその機能性のみならず，アルゴリズムのセキュリティへの配慮の重要性に対する認識が高まっている．本講演では，
機械学習アルゴリズムに対する攻撃手法を紹介するとともに，話者認識におけるMAP適応を例にあげて，その理論的脆弱性の解析を元に，登録された全ての話者に対して，漸近的に100％の確率でなりすますことができるウルフ攻撃の実現可能性について紹介する．
また，このような攻撃を含む未知のなりすまし攻撃を検知するための取り組みについて紹介する．

経歴
2010年早稲田大学博士（工学）。早稲田大学理工学研究所
嘱託研究員、同研究所招聘研究員、同研究所次席研究員（研究員助教）を経て、2017年4月より静岡大学情報学部情報科学科/総合科学技術研究科 情報学専攻 講師。生体認証、ネットワークセキュリティ、機械の人間との間に生じる関係とそのセキュリティ問題に関する研究に従事。

企画１：国際学会既発表セッション(ポスター発表)
Yusuke Yasuda, Xin Wang, Shinji Takaki, Junichi Yamagishi (NII),“Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention
for pitch accent language”
Abstract: End-to-end speech synthesis is a promising approach that directly converts raw text tospeech. Japanese could be one of the most difficult languages for which to achieve end-to-end
speech synthesis in two reasons. One reason is its character diversity, and the other is its pitchaccents. Therefore, state-of-the-art systems are still based on a traditional pipeline frameworkthat requires a separate text analyzer and duration model. As a first step towards end-to-end
speech synthesis, this research focus on pitch accent in Japanese language. We propose a newarchitecture that extends Tacotron with self-attention to capture long-term dependencies related topitch accents. A large-scale listening test show the proposed system outperforms baseline
Tacotron. In addition, we investigated the impacts of the presence of accentual-type labels, the useof force or predicted alignments, and acoustic features used as local condition parameters of the
Wavenet vocoder. Our results reveal that although the proposed systems still do not match thequality of a top-line pipeline system for Japanese, we show important stepping stones towards
end-to-end Japanese speech synthesis. (Accepted for ICASSP 2019)
Yi Zhao, Shinji Takaki, Hieu-Thi Luong, Junichi Yamagishi (NII), Daisuke Saito, Nobuaki
Minematsu (University of Tokyo)“Wasserstein GAN and Waveform Loss-based Multi-speaker Acoustic Model Training for
Text-to-Speech System”
Abstract: Recent neural networks directly learned from speech waveform samples such as
WaveNet and sampleRNN have achieved very high-quality synthetic speech in terms of bothnaturalness and speaker similarity even in multi-speaker text-to-speech synthesis systems. Suchthe neural networks are used as a replacement of vocoder and hence they are often called neuralvocoder. The neural vocoder uses acoustic features as local condition parameters, which need tobe accurately predicted by another acoustic model. However it is not fully investigated how weshould train the acoustic model that predicts the local condition parameters and the final quality ofsynthetic speech are significantly affected by the performance of the acoustic model. The
significant degradation happens especially when predicted acoustic features has mismatchedcharacteristics compared to natural ones. In order to reduce the mismatched characteristics
between natural and generated acoustic features, this paper proposes frameworks which
incorporates either conditional generative adversarial networks (GANs) or its variant called
Wasserstein GAN with gradient penalty (WGAN-GP) into multi-speaker speech synthesis that uses
the Wavenet vocoder. Furthermore, this paper extends the GAN frameworks and uses thediscretized mixture logistic loss of a well-trained WaveNet as well as mean squared error andadversarial losses as parts of objective functions. Experimental results show that acoustic models
trained using the WGAN-GP framework using back propagated DML loss can achieve highest
subjective evaluation scores in terms of both quality and speaker similarity. (Published at IEEE
Access)Tifani Warnita, Nakamasa Inoue, Koichi Shinoda (Tokyo Institute of Technology),“Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio
Data”
Abstract: Early prediction of Alzheimer's disease has the major importance for taking anappropriate and quick treatment in order to prevent the disease become worse. In this paper, we
propose our linguistic-independent approach for detecting dementia by utilizing only speech data
whereas most of the previous study using linguistic information in their approach. We extract theparalinguistic feature set for each utterance of the patient then do the classification using Gated
Convolutional Neural Network (GCNN) with a majority voting mechanism for the final verdict. We
evaluated our method using Pitt Corpus and yield the accuracy of 73.6%, which is better than theconventional sequential minimal optimization (SMO) by 7.6 points. This GCNN can be trained witha relatively small amount of data and can capture the temporal information in audio paralinguisticfeatures. Furthermore, since we do not use any linguistic features, our approach has theadvantage of being more easily applicable in various languages. (Published at Interspeech 2018)
Fuming Fang, Xin Wang, Junichi Yamagishi , Isao Echizen (NII),“Audiovisual speaker conversion: jointly and simultaneously transforming facial
expression and acoustic characteristics”
Abstract: We present an audiovisual speaker conversion method, which jointly and simultaneouslytransforms both facial expressions and voice of a source speaker into those of a target speaker.
Since facial and acoustic features are highly correlated together by the proposed method, it would
allow them to compensate for each other and thus the converted target speaker appears andsounds natural. We used a neural network to convert facial and acoustic features and then used a
WaveNet and an image-reconstruction network to generate waveform and RGB image from boththe converted features. Experimental results showed that the proposed method achieved better
naturalness and speaker similarity compared with one that separately transformed facial andacoustic features. (Accepted for ICASSP 2019)
Yilong Peng, Hayato Shibata, Takahiro Shinozaki (Tokyo Institute of Technology)“Reward Only Training of Encoder-Decoder Digit Recognition Systems Based on Policy
Gradient Methods”
Abstract: Recently, zero resource speech recognition has gotten more popular not only in realistic
engineering but also in scientific purposes. However, existing unsupervised learning methods thatuse speech input only are unable to associate speech to its corresponding text. In this paper, we
propose an approach that assumes a scalar reward is given for each decoded result, use it to train
the system in reinforcement learning. Focusing on encoder-decoder based speech recognitionneural network system, we find the difficulty is to obtain a convergence without the help ofsupervised learning. Towards this problem, we explore on several neural network architectures,
optimization methods and reward definitions, seeking a suitable configuration for policy gradientreinforcement learning. We performed experiments on connected digit utterances from the
TIDIGITS corpus and reduce the digit error rate to 13.6% on our best performed digit recognition
system, reveal the appropriate condition for unsupervised reinforcement learning and shows it islargely different from supervised training. (Published at APSIPA 2018)
Tomohiro Tanaka and Takahiro Shinozaki (Tokyo Institute of Technology)“End-to-End Training of Keyword Detection Neural Networks Using F-Measure Objective
and 2D-RNN”
Abstract: Acoustic embedding based keyword detection neural network models have been showing
excellent performance. In this work, we propose two end-to-end continuous keyword detectors thatwork without assuming the segment boundaries of keywords in the input sequence. One is anextension of the conventional long short term memory (LSTM) based embedding approachremoving the segmentation assumption, and the other is an extension of continuous dynamic
programming (DP) matching to an end-to-end neural network by using a two-dimensional recurrent
neural network (2D-RNN). For the training, we propose and investigate a soft decision version of
the F-measure as the objective function in addition to the cross-entropy measure to use aconsistent evaluation measure in the training and the evaluation. Experiments using the WSJ
corpus show the 2D-RNN based continuous DP matching has much higher performance than theembedding based detector and posteriogram feature based conventional continuous DP matching.
(Published at APSIPA 2018)
Bairong Zhuang, Wenbo Wang and Takahiro Shinozaki (Tokyo Institute of Technology)“Investigation of Attention-Based Multimodal Fusion and Maximum Mutual Information
Objective for DSTC7 Track3”
Abstract: In this paper, we show our investigation on the Audio Visual Scene-aware dialog (AVSD)
task which is proposed in DSTC7. We investigate the effectiveness of different modality fusion
methods as well as different input modalities. We also employ the Maximum Mutual
Information(MMI) objective as the objective for the AVSD system. Our experiments shows thesystem that uses MMI as the objective obtains 6.6% relative improvement over the baseline
system on BLEU. (Published at DSTC7 workshop: Dialog System Technology Challenges)
Yi-Chiao Wu, Kazuhiro Kobayashi, Tomoki Hayashi, Patrick Lumban Tobing, and Tomoki
Toda (Nagoya University)“Collapsed speech segment detection and suppression for WaveNet vocoder”
Abstract: In this paper, we propose a collapsed waveform detection and refinement framework for
the WaveNet vocoder which is one of the state-of-the-art neural network-based vocoders. Although
the WaveNet vocoder generates speech with high-fidelity conditioning on the natural acoustic
features, it is hard to deal with the unseen acoustic features. That is, the WaveNet vocoder
sometimes generates very noisy speech segments when conditioning on the outside testing
features such as voice converted or speech enhanced ones. To address this problem, we first
design a defective speech detector, which uses a waveform envelope detection technique to detect
the collapsed speech segments. Then the WaveNet vocoder regenerate this unexpected segmentswith the proposed linear predictive coding (LPC) coefficients-constraint, which refine the output
distortion from the WaveNet vocoder to avoid generating the collapsed speech. The verification
objective evaluation results indicates the effectiveness of the proposed detection method which
achieves about 10% equal error rate. Furthermore, the quality and speaker similarity subjective
test are also conducted, and the results demonstrate the proposed framework can improve thespeech quality while maintain the same speaker similarity as the original WaveNet vocoder.
(Published at Interspeech 2018)
Hitoshi Suda, Gaku Kotani, Shinnosuke Takamichi, and Daisuke Saito (Tokyo University)“A Revisit to Feature Handling for High-quality Voice Conversion Based on Gaussian
Mixture Model”
Abstract: This paper discusses influences of handling acoustic features on the quality of generated
sounds in voice conversion systems based on Gaussian mixture models. This paper also
introduces an alternative wave generation method, which is named SP-WORLD, inspired byWORLD vocoder framework, and which outperforms conventional MLSA filtering in some cases.
(Published at APSIPA 2018)
Hieu-Thi Luong and Junichi Yamagishi (NII)“Scaling and bias codes for modeling speaker-adaptive DNN-based speech synthesis
systems”
Abstract: Augmenting a speaker embedding vector to the linguistic input of a neural network is a
popular method for modeling multi-speaker speech synthesis systems. This setup also allow themodel to be quickly adapted to unseen speakers. However by first systematically reviewing thecore principles of neural-network based speaker-adaptive models, we show that the speakerembedding method is constrained by its own nature as bias adaptation. Furthermore we propose
to expand the concept of the speaker embedding to scaling and bias operations in order to add a
new degree of freedom for adaptation process. The experiment results showed that the proposed
method improved the performance of speaker adaptation to the unseen speaker compared with theconventional input code method. (Published at SLT 2018)
Patrick Lumban Tobing, Tomoki Hayashi, Yi-Chiao Wu, Kazuhiro Kobayashi, and Tomoki
Toda (Nagoya University)“Voice Conversion with Fine-Tuned WaveNet based on Concatenated Spectral Mappings
using Recurrent Neural Network”
Abstract: In this work, we propose a voice conversion (VC) framework with the use of
concatenated recurrent neural network (RNN)-based spectral mappings and finely-tuned WaveNet
vocoder. It is well known that with the use of distorted (oversmoothed) speech features, such as
spectral parameters estimated from a statistical mapping model, WaveNet suffers from quality
degradation. This is due to the mismatches between the natural spectral parameters used indeveloping the WaveNet model and the estimated features used in the generation time. In VC, it is
not straightforward to use oversmoothed features in WaveNet development because the timesequence alignment of the speech signals between the source and the target speakers is different.

To overcome this issue, we propose to develop RNN-based spectral mapping models for each of
the target-to-source mapping and the source-to-target mapping. Hence, to obtain the oversmoothed features for WaveNet development, the target-to-source and the source-to-target
mapping models are concatenated to produce estimated target features with the alignment of thetarget speaker. A pre-trained WaveNet model is then fine-tuned to be adapted with the oversmoothed target spectral features. In the generation time, the source-to-target mapping model isused to generate estimated spectral features to be fed into the fine-tuned WaveNet vocoder. The
experimental results demonstrate the effectiveness of the proposed method in improving thenaturalness of the converted waveform, even if compared with the use of a post-conversion
processing, based on spectrum differential and global variance, which is used to alleviate the oversmoothing. (Published at SLT 2018)
Yuki Takashima, Tetsuya Takiguchi, Yasuo Ariki (Kobe University)“Exemplar-based Lip-to-Speech Synthesis Using Convolutional Neural Networks”
Abstract: This paper proposes a neural network-based lip-to-speech synthesis approach thatconverts “unvoiced" lip movements to “voiced” utterances. We build on our recently proposed
exemplar-based non-negative matrix factorization approach by addressing several of its
shortcomings. First, the original model imposes unnatural constraints on the preprocessing ofvisual features in order to satisfy the non-negativity constraint of NMF. Second, there is a possibility
that an activity matrix cannot be shared between the visual and the audio feature in an NMF-based
approach. To tackle these problems, in this paper, we propose a new method that employs
convolutional neural networks to convert visual features into audio features, and also integrates an
exemplar-based approach into the neural networks in order to combine the advantages of our
proposed approach with the flexibility of neural network approaches. Experimental results showedthat our proposed method produced more proper spectra than conventional NMF-based methods.
(Accepted for IW-FCV 2019)企画２：Interspeech2018報告会 (口頭発表)
SIG-SLPの2月の研究会では、ここ数年、INTERSPEECHの論文紹介セッションを開催しています。2019年の2月研究会では、９月にインドにて開催された国際会議Interspeech2018において発表された以下の論文を紹介する予定です。
・音源分離
担当：俵直弘(早稲田大学)
紹介予定論文1.

Z. Wang et al, End-to-End Speech Separation with Unfolded Iterative Phase
Reconstruction

2.

Z. Meng et al, Cycle-consistent speech enhancement

3.

Z. Wang et al, Integrating spectral and spatial features for multi-channel speakerseparation

・音声認識
担当：増村亮(日本電信電話株式会社)鈴木雅之(日本IBM)福田隆(日本IBM)
紹介予定論文4.

S. Kim et. al, Improved Training for Online End-to-end Speech Recognition Systems

5.

R. Pang et. al, Compression of End-to-End Models

6.

A. Zeyer et. al, Improved Training of End-to-end Attention Models for Speech Recognition

7.

A. Sriram et al, Cold Fusion: Training Seq2Seq Models Together with Language Models

8.

KC. Sim et al, Domain Adaptation Using Factorized Hidden Layer for Robust Automatic
Speech Recognition

9.

J. Andrés-Ferrer et al, Efficient language model adaptation with Noise Contrastive
Estimation and Kullback-Leibler regularization

10. B. Milde et al, Unspeech: Unsupervised Speech Context Embeddings
11. D. Liu et al, Completely Unsupervised Phoneme Recognition by Adversarially Learning
Mapping Relationships from Audio Embeddings
12. .Y Chung et al, Speech2Vec: A Sequence-to-Sequence Framework for Learning Word
Embeddings from Speech
・医療・支援技術
担当：越智景子(東京工科大学)

紹介予定論文
13. P. Kothalker et al, Fusing text-dependent word-level i-vector models to screen 'at risk' childspeech
14. B. Mirheidari et al, Detecting signs of dementia using word vector representations

・話者照合，感情認識
担当：安藤厚志(日本電信電話株式会社)，
塩田さやか(首都大学東京)
紹介予定論文15. Y. Zhu et al, Self-attentive Speaker Embeddings for Text-Independent Speaker Verification,
16. Z. Huang et al, Angular Softmax for Short-Duration Text-independent Speaker Verification
17. W. Ding et al, MTGAN: Speaker Verification through Multitasking Triplet Generative
Adversarial Networks
18. H. Murthy et al, Decision-level featureswitching as a paradigm for replay attack detection
19. C. Wijenayake et al, Modulation Dy-namic Features for the Detection of Replay Attacks
20. E. Ambikairajah et al, Deep Siamese Architecture Based Re-play Detection for Secure
Voice Biometric,
21. M. Sarma et al, Emotion Identification from Raw Speech Signals Using DNNs,
22. P. Yenigalla et al, Speech Emotion Recognition Using Spectrogram & Phoneme
Embedding
23. S. Sahu et al, On Enhancing Speech Emotion Recognition using Generative Adversarial
Networks
24. S. Parthasarathy et al, Ladder Networks for Emotion Recognition: Using Unsupervised
Auxilary Tasks to Improve Predictions of Emotional Attributes
・音声合成
担当：高木信二(NII)
玉森聡(愛知工業大学)，
沢田慶(マイクロソフトディベロップメント株式会社)
紹介予定論文25. B. Sisman et al, A Voice Conversion Framework with Tandem Feature Sparse
Representation and Speaker-Adapted WaveNet Vocoder
26. L. Liu et al, WaveNet Vocoder with Limited Training Data for Voice Conversion
27. Y. Wu et al, Collapsed Speech Segment Detection and Suppression for WaveNet Vocoder
28. L. Juvela et al, Speaker-independent Raw Waveform Model for Glottal Excitation
29. Z. Hodari et al, Learning Interpretable Control Dimensions for Speech Synthesis by Using
External Data,
30. K. Akuzawa et al, Expressive Speech Synthesis via Modeling Expressions with Variational
Autoencoder,
31. H. Li et al, EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech
Synthesis System
32. A. Baird et al, Perception and analysis of the likeability and human likeness of synthesized
speech
33. Ruibo Fu et al, Transfer Learning Based Progressive Neural Networks for Acoustic
Modeling in Statistical Parametric Speech Synthesis
34. K. Chen et al, High quality voice conversion using spectrogram based Wavenet
都合により、アナウンスなく紹介する論文は変更になることがあります。

一般講演１(口頭発表, 英語)
発表時間：2/27 13:00-13:25
郡山 知樹 (東京工業大学), 高道 慎之介 (東京大学大学院), 小林 隆夫 (東京工業大学)“GMMNに基づく音声合成におけるグラム行列のスパース近似の検討”
Abstract: 生成的モーメントマッチングネット(GMMN)では音声のランダムな特徴を合成できるが，目的関数であるmaximum mean discrepancy(MMD)は計算量の点から直接の計算が不可能であり近似が必要である．しかし，MMDの近似方法について詳細な検討は行われてこなかった．本研
究ではMMDに用いるグラム行列をスパース近似する手法を検討し，その性能について自然性およびランダム性の観点から評価する．
(英語による発表を予定)
発表時間：2/27 13:25-13:50
Yi Liu (Tokyo Institute of Technology), Takahiro Shinozaki (Tokyo Institute of Technology)“I-vector Domain Adaptation Using Cycle-Consistent Adversarial Networks for Speaker
Recognition”
Abstract: Speaker recognition systems often suffer from severe performance degradation due tothe difference between training data and evaluation data, which is called domain mismatch
problem. In this paper, inspired by adversarial strategies in deep learning techniques, we propose
a cycle-consistent adversarial networks-based domain adaptation method. This method performs
an i-vector domain transformation from source (out) domain to target (in) domain to reduce thedomain mismatch. Additionally, it uses a cycle structure that reduces the negative influence oflosing speaker information in i-vectors during the transformation and makes it possible to use
unpaired datasets for training. The experimental result shows that the speaker recognition systemusing the proposed method obtains a better performance compared with the conventional i-vector
and PLDA based speaker recognition system.
発表時間：2/27 13:50-14:15
Xin Wang (NII), Shinji Takaki (NII), Junichi Yamagishi (NII)“Investigating neural source-filter waveform model for statistical parametric speechsynthesis”
Abstract: Recently we proposed the neural source-filter model (NSF) that converts a sequence of
acoustic features into a speech waveform. Similar to other recent neural waveform models, the
NSF is a non-autogressive model powered by dilated CNN; however, the NSF uses the sine
waveform instead of the random noise as the excitation. Furthermore, without using thenormalizing flow, the NSF simply optimizes the network parameters by minimizing a spectral
amplitude distance. In this work, we further investigated the three issues: whether the network
structure can be further simplified; whether the NSF can be applied to multi-speaker synthesis;
whether the NSF can be applied to convert the linguistic features into the speech waveforms. Our
experiments showed positive results on all the three points. Particularly, we found that the
WaveNet-style gated activation can be safely removed and the NSF performs quite well as a pure
dilated-CNN-based network.

一般講演２(口頭発表, 日本語)
発表時間：2/28 10:00-10:25
森川 寛也（クリスタルメソッド株式会社）三橋 晟（クリスタルメソッド株式会社）河合 継（クリスタルメソッド株式会社）能勢 隆（東北大学大学院工学研究科・工学部）千葉 祐弥（東北大学大学院工学研究科・工学部）“テキストと音声のマルチモーダル感情認識”
Abstract: 本論文では，音響情報による感情識別の結果と言語情報による感情識別の結果を結合した感情認識の手法を提案する． 具体的には音声を音響的な情報と言語的な情報に分けてそれぞれの感情識別器を作り， 2つの識別結果を結合して感情識別を行うという方法である． 音響情報の感情識別器については，文章を単語ごとに区切り１単語１セルとしてLSTMへ入力し学習させる
方法と， 区切らずに学習させる方法とを比較した． 結果としては現在の時点では区切らないほうが精度が高いが， 今後データ量の増加に伴い区切った方が良い精度を出すと予想できる結果となった． また，言語と音響の識別結果を結合したモデルではテストデータに対して，75.5%の精度で感情を識別することができた． これは音響情報のみと言語情報のみの識別結果よりも精度が高かった． 今後の課題としてさらに精度を上げていくには大規模コーパスを作成か， データ拡張によってデータを増やしていく必要があることが分かった．
発表時間：2/28 10:25-10:50
高道 慎之介（東京大学）猿渡 洋（東京大学）“正弦関数摂動von Mises分布DNNのモード近似を用いた位相復元”
Abstract: Deep neural networkを用いた振幅スペクトログラムからの位相復元を提案する．正弦関数摂動von Mises分布DNNは音声の群遅延のモデル化に適しているが，その分布のモードが解析的に求まらないため，マルチタスク学習への応用が困難である．本稿では，分布のモードを微分可能かつ解析的な形で近似することで，マルチタスク学習及び高精度な位相復元を可能にする．

2018年 SIG-SLP企業賞
2018年SIG-SLP企業賞は、昨年SLP研究会で発表された学生論文のうち、もっとも優れた論文に送られる賞です。SIG-SLPのダイヤモンドレベル企業スポンサー名をお借りし、以下の最優秀
学生論文２編に企業賞を授与します。選考委員会は音声言語情報処理研究会運営委員および該
当企業の担当者により構成され、選考委員会委員の投票により決定されました。

Fairly Devices賞
End-to-End音声合成を用いた単語単位End-to-End音声認識のデータ拡張
上乃 聖, 三村 正人, 坂井 信輔, 河原 達也 (京都大学情報学研究科)
2018-SLP-125
2018/12/3
Yahoo! JAPAN賞
条件付き敵対的生成ネットワークを用いたデータ拡張による対話行為分類法の検討
河野 誠也, 吉野 幸一郎, 中村 哲 (奈良先端科学技術大学院大学)
2018-SLP-125
2018/12/3

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★情報処理学会

音声言語情報処理研究会（SLP）

主査
西村 雅史（静岡大）幹事福田 隆（日本IBM）
山岸 順一（NII）
塩田 さやか（首都大東京）
俵 直弘（早稲田大）

問い合わせ先国立情報学研究所 山岸順一
jyamagis at nii.ac.jp

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