26.1.12.3 Speech Synthesis, Synthetic Speech

Chapter Contents (Back)
Speech. Speech Synthesis. Synthesis, Speech.

Yeh, C.Y., Hwang, S.H.,
Efficient text analyser with prosody generator-driven approach for Mandarin text-to-speech,
VISP(152), No. 6, December 2005, pp. 793-799.
DOI Link 0512
BibRef

Chouireb, F.[Fatima], Guerti, M.[Mhania],
Towards a high quality Arabic speech synthesis system based on neural networks and residual excited vocal tract model,
SIViP(2), No. 1, January 2008, pp. 73-87.
Springer DOI 0712
BibRef

Elfitri, I., Gunel, B., Kondoz, A.M.,
Multichannel Audio Coding Based on Analysis by Synthesis,
PIEEE(99), No. 4, April 2011, pp. 657-670.
IEEE DOI 1103
Part of 3-D display series. BibRef

Jung, C.S.[Chi-Sang], Joo, Y.S.[Young-Sun], Kang, H.G.[Hong-Goo],
Waveform Interpolation-Based Speech Analysis/Synthesis for HMM-Based TTS Systems,
SPLetters(19), No. 12, December 2012, pp. 809-812.
IEEE DOI 1212
BibRef

Carmona, J.L., Barker, J., Gomez, A.M., Ma, N.[Ning],
Speech Spectral Envelope Enhancement by HMM-Based Analysis/Resynthesis,
SPLetters(20), No. 6, 2013, pp. 563-566.
IEEE DOI speech enhancement 1307
BibRef

Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., Oura, K.,
Speech Synthesis Based on Hidden Markov Models,
PIEEE(100), No. 5, May 2013, pp. 1234-1252.
IEEE DOI 1305
BibRef

Ling, Z., Kang, S., Zen, H., Senior, A., Schuster, M., Qian, X., Meng, H., Deng, L.,
Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends,
SPMag(32), No. 3, May 2015, pp. 35-52.
IEEE DOI 1504
Acoustic signal detection BibRef

Bordel, G., Penagarikano, M., Rodriguez-Fuentes, L.J., Alvarez, A., Varona, A.,
Probabilistic Kernels for Improved Text-to-Speech Alignment in Long Audio Tracks,
SPLetters(23), No. 1, January 2016, pp. 126-129.
IEEE DOI 1601
Acoustics BibRef

Ninh, D.K.[Duy Khanh], Yamashita, Y.[Yoichi],
F0 Parameterization of Glottalized Tones in HMM-Based Speech Synthesis for Hanoi Vietnamese,
IEICE(E98-D), No. 12, December 2015, pp. 2280-2289.
WWW Link. 1601
BibRef

Erro, D.,
Two-Band Radial Postfiltering in Cepstral Domain with Application to Speech Synthesis,
SPLetters(23), No. 2, February 2016, pp. 202-206.
IEEE DOI 1602
filtering theory BibRef

Hu, Y.J., Ling, Z.H.,
DBN-based Spectral Feature Representation for Statistical Parametric Speech Synthesis,
SPLetters(23), No. 3, March 2016, pp. 321-325.
IEEE DOI 1603
belief networks BibRef

Tsiaras, V., Maia, R., Diakoloukas, V., Stylianou, Y., Digalakis, V.,
Global Variance in Speech Synthesis With Linear Dynamical Models,
SPLetters(23), No. 8, August 2016, pp. 1057-1061.
IEEE DOI 1608
speech synthesis BibRef

Wang, F.Z.[Fang-Zhou], Nagano, H.[Hidehisa], Kashino, K.[Kunio], Igarashi, T.[Takeo],
Visualizing Video Sounds With Sound Word Animation to Enrich User Experience,
MultMed(19), No. 2, February 2017, pp. 418-429.
IEEE DOI 1702
BibRef

Sharma, B., Prasanna, S.R.M.,
Enhancement of Spectral Tilt in Synthesized Speech,
SPLetters(24), No. 4, April 2017, pp. 382-386.
IEEE DOI 1704
speech enhancement BibRef

Singh, R.[Rita], Jiménez, A.[Abelino], Řland, A.[Anders],
Voice disguise by mimicry: deriving statistical articulometric evidence to evaluate claimed impersonation,
IET-Bio(6), No. 4, July 2017, pp. 282-289.
DOI Link 1707
BibRef

Lee, K.S.,
Restricted Boltzmann Machine-Based Voice Conversion for Nonparallel Corpus,
SPLetters(24), No. 8, August 2017, pp. 1103-1107.
IEEE DOI 1708
Boltzmann machines, probability, speaker recognition, OGI VOICES corpus, conversion function, linear transformation, parallel training corpus. BibRef

Reddy, M.K., Rao, K.S.,
Robust Pitch Extraction Method for the HMM-Based Speech Synthesis System,
SPLetters(24), No. 8, August 2017, pp. 1133-1137.
IEEE DOI 1708
feature extraction, hidden Markov models, speech synthesis, wavelet transforms, CMU Arctic and Keele databases, HMM-based speech synthesis system, continuous wavelet transform coefficients, hidden Markov model-based HTS, pitch estimation, pitch tracking, robust pitch extraction method, speech representation, BibRef

Liu, Z.C., Ling, Z.H., Dai, L.R.,
Statistical Parametric Speech Synthesis Using Generalized Distillation Framework,
SPLetters(25), No. 5, May 2018, pp. 695-699.
IEEE DOI 1805
Fourier transforms, acoustic signal processing, learning (artificial intelligence), recurrent neural nets, speech synthesis BibRef

Drugman, T., Huybrechts, G., Klimkov, V., Moinet, A.,
Traditional Machine Learning for Pitch Detection,
SPLetters(25), No. 11, November 2018, pp. 1745-1749.
IEEE DOI 1811
acoustic signal processing, estimation theory, feature extraction, learning (artificial intelligence), speech synthesis BibRef

Arik, S.Ö., Jun, H., Diamos, G.,
Fast Spectrogram Inversion Using Multi-Head Convolutional Neural Networks,
SPLetters(26), No. 1, January 2019, pp. 94-98.
IEEE DOI 1901
audio signal processing, feedforward neural nets, interpolation, iterative methods, learning (artificial intelligence), speech synthesis BibRef

Masuyama, Y., Yatabe, K., Oikawa, Y.,
Griffin-Lim Like Phase Recovery via Alternating Direction Method of Multipliers,
SPLetters(26), No. 1, January 2019, pp. 184-188.
IEEE DOI 1901
acoustic signal processing, iterative methods, optimisation, subjective test, objective measure, ADMM, signal recovery, STFT-based speech synthesis BibRef

Kwon, O., Jang, I., Ahn, C., Kang, H.,
An Effective Style Token Weight Control Technique for End-to-End Emotional Speech Synthesis,
SPLetters(26), No. 9, September 2019, pp. 1383-1387.
IEEE DOI 1909
Speech synthesis, Spectrogram, Training, Decoding, Acoustics, Vocoders, emotion weight values BibRef

Liu, Q., Jackson, P.J.B., Wang, W.,
A Speech Synthesis Approach for High Quality Speech Separation and Generation,
SPLetters(26), No. 12, December 2019, pp. 1872-1876.
IEEE DOI 2001
decoding, source separation, speech coding, speech synthesis, time-domain analysis, time-domain samples, high quality BibRef

Cotescu, M., Drugman, T., Huybrechts, G., Lorenzo-Trueba, J., Moinet, A.,
Voice Conversion for Whispered Speech Synthesis,
SPLetters(27), 2020, pp. 186-190.
IEEE DOI 2002
Whispered speech conversion, voice conversion (VC), whispered text to speech (TTS) BibRef

Aylett, M.P., Vinciarelli, A., Wester, M.,
Speech Synthesis for the Generation of Artificial Personality,
AffCom(11), No. 2, April 2020, pp. 361-372.
IEEE DOI 2006
Speech, Speech synthesis, Robots, Psychology, Hidden Markov models, Digital signal processing, Computational modeling, Personality, automatic personality synthesis BibRef

Rao, M.V.A.[M.V. Achuth], Ghosh, P.K.[Prasanta Kumar],
SFNet: A Computationally Efficient Source Filter Model Based Neural Speech Synthesis,
SPLetters(27), 2020, pp. 1170-1174.
IEEE DOI 2007
Neural vocoder, source-filter model, computational complexity, Mel-spectrum BibRef

Zhou, Y., Tian, X., Li, H.,
Multi-Task WaveRNN With an Integrated Architecture for Cross-Lingual Voice Conversion,
SPLetters(27), 2020, pp. 1310-1314.
IEEE DOI 2008
Vocoders, Pipelines, Generators, Linguistics, Acoustics, Training, Task analysis, Cross-lingual voice conversion (xVC), integrated architecture BibRef

Yang, J.C.[Ji-Chen], Lin, P.[Pei], He, Q.H.[Qian-Hua],
Constant-Q magnitude-phase coefficients extraction for synthetic speech detection,
IET-Bio(9), No. 5, September 2020, pp. 216-221.
DOI Link 2008
BibRef

Liu, R., Sisman, B., Bao, F., Gao, G., Li, H.,
Modeling Prosodic Phrasing With Multi-Task Learning in Tacotron-Based TTS,
SPLetters(27), 2020, pp. 1470-1474.
IEEE DOI 2009
Task analysis, Generators, Training, Speech synthesis, Decoding, Linguistics, Data models, Tacotron, multi-task learning, prosody BibRef

Qi, J., Du, J., Siniscalchi, S.M., Ma, X., Lee, C.,
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression,
SPLetters(27), 2020, pp. 1485-1489.
IEEE DOI 2009
Upper bound, Speech enhancement, Additive noise, Complexity theory, Laplace equations, Neural networks, Loss measurement, vector-to-vector regression BibRef

Yang, S., Wang, Y., Xie, L.,
Adversarial Feature Learning and Unsupervised Clustering Based Speech Synthesis for Found Data With Acoustic and Textual Noise,
SPLetters(27), 2020, pp. 1730-1734.
IEEE DOI 1806
Noise measurement, Decoding, Speech synthesis, Speech recognition, Training, Speech enhancement, Acoustics, Adversarial training, speech synthesis BibRef

Lee, J.Y., Cheon, S.J., Choi, B.J., Kim, N.S.,
Memory Attention: Robust Alignment Using Gating Mechanism for End-to-End Speech Synthesis,
SPLetters(27), 2020, pp. 2004-2008.
IEEE DOI 2012
Logic gates, Speech synthesis, Decoding, Speech recognition, Training, Computational modeling, Memory management, memory attention BibRef

Zhang, Y.[You], Jiang, F.[Fei], Duan, Z.Y.[Zhi-Yao],
One-Class Learning Towards Synthetic Voice Spoofing Detection,
SPLetters(28), 2021, pp. 937-941.
IEEE DOI 2106
Training, Signal processing algorithms, Speech synthesis, Feature extraction, Cepstral analysis, Support vector machines, speaker verification BibRef

Saeki, T.[Takaaki], Takamichi, S.[Shinnosuke], Saruwatari, H.[Hiroshi],
Incremental Text-to-Speech Synthesis Using Pseudo Lookahead With Large Pretrained Language Model,
SPLetters(28), 2021, pp. 857-861.
IEEE DOI 2106
Context modeling, Training, Tuning, Speech synthesis, Predictive models, Linguistics, Decoding, contextual embedding BibRef

Comanducci, L.[Luca], Bestagini, P.[Paolo], Tagliasacchi, M.[Marco], Sarti, A.[Augusto], Tubaro, S.[Stefano],
Reconstructing Speech From CNN Embeddings,
SPLetters(28), 2021, pp. 952-956.
IEEE DOI 2106
Decoding, Task analysis, Spectrogram, Feature extraction, Image reconstruction, Training, speech recognition BibRef

Hua, G.[Guang], Teoh, A.B.J.[Andrew Beng Jin], Zhang, H.J.[Hai-Jian],
Towards End-to-End Synthetic Speech Detection,
SPLetters(28), 2021, pp. 1265-1269.
IEEE DOI 2107
Feature extraction, Speech synthesis, Training, Mel frequency cepstral coefficient, Task analysis, Standards, end-to-end BibRef

Cheon, S.J.[Sung Jun], Choi, B.J.[Byoung Jin], Kim, M.[Minchan], Lee, H.[Hyeonseung], Kim, N.S.[Nam Soo],
A Controllable Multi-Lingual Multi-Speaker Multi-Style Text-to-Speech Synthesis With Multivariate Information Minimization,
SPLetters(29), 2022, pp. 55-59.
IEEE DOI 2202
Training, Upper bound, Speech synthesis, Correlation, Mutual information, Synthesizers, Estimation, Disentanglement, total correlation BibRef

Bilbao, S.[Stefan],
3D Interpolation in Wave-Based Acoustic Simulation,
SPLetters(29), 2022, pp. 384-388.
IEEE DOI 2202
Interpolation, Solid modeling, Numerical models, Mathematical models, Time-domain analysis, wave-based simulation BibRef

Saleem, N.[Nasir], Gao, J.[Jiechao], Irfan, M.[Muhammad], Verdu, E.[Elena], Fuente, J.P.[Javier Parra],
E2E-V2SResNet: Deep residual convolutional neural networks for end-to-end video driven speech synthesis,
IVC(119), 2022, pp. 104389.
Elsevier DOI 2202
Video processing, E2E speech synthesis, ResNet-18, Residual CNN, Waveform CRITIC BibRef

Sun, X.[Xiao], Li, J.Y.[Jing-Yuan], Tao, J.H.[Jian-Hua],
Emotional Conversation Generation Orientated Syntactically Constrained Bidirectional-Asynchronous Framework,
AffCom(13), No. 1, January 2022, pp. 187-198.
IEEE DOI 2203
Decoding, Dictionaries, Syntactics, Sun, Computational modeling, Detectors, Indexes, Emotional conversation generation, affective computing BibRef

Liu, S.G.[Shi-Guang], Li, S.[Sijia], Cheng, H.[Haonan],
Towards an End-to-End Visual-to-Raw-Audio Generation With GAN,
CirSysVideo(32), No. 3, March 2022, pp. 1299-1312.
IEEE DOI 2203
Videos, Visualization, Task analysis, Computational modeling, Animation, Acoustics, Synchronization, Visual to audio, cross media, audio-visual synchronization BibRef

Li, C.T.[Chang-Tao], Yang, F.[Feiran], Yang, J.[Jun],
The Role of Long-Term Dependency in Synthetic Speech Detection,
SPLetters(29), 2022, pp. 1142-1146.
IEEE DOI 2205
Transformers, Convolution, Feature extraction, Training, Speech synthesis, Voice activity detection, voice anti-spoofing BibRef

Cui, S.S.[San-Shuai], Huang, B.Y.[Bing-Yuan], Huang, J.W.[Ji-Wu], Kang, X.G.[Xian-Gui],
Synthetic Speech Detection Based on Local Autoregression and Variance Statistics,
SPLetters(29), 2022, pp. 1462-1466.
IEEE DOI 2207
Feature extraction, Speech synthesis, Standards, Filtering, Forensics, Mel frequency cepstral coefficient, Windows, variance statistics BibRef

Lei, Y.[Yi], Yang, S.[Shan], Zhu, X.F.[Xin-Fa], Xie, L.[Lei], Su, D.[Dan],
Cross-Speaker Emotion Transfer Through Information Perturbation in Emotional Speech Synthesis,
SPLetters(29), 2022, pp. 1948-1952.
IEEE DOI 2209
Timbre, Spectrogram, Perturbation methods, Generators, Speech synthesis, Adaptation models, Acoustics, speech synthesis BibRef

Choi, B.J.[Byoung Jin], Jeong, M.[Myeonghun], Lee, J.Y.[Joun Yeop], Kim, N.S.[Nam Soo],
SNAC: Speaker-Normalized Affine Coupling Layer in Flow-Based Architecture for Zero-Shot Multi-Speaker Text-to-Speech,
SPLetters(29), 2022, pp. 2502-2506.
IEEE DOI 2212
Hidden Markov models, Couplings, Training, Adaptation models, Jacobian matrices, Standards, Predictive models, zero-shot multi-speaker text-to-speech BibRef

Chen, L.C.[Li-Chin], Chen, P.H.[Po-Hsun], Tsai, R.T.H.[Richard Tzong-Han], Tsao, Y.[Yu],
EPG2S: Speech Generation and Speech Enhancement Based on Electropalatography and Audio Signals Using Multimodal Learning,
SPLetters(29), 2022, pp. 2582-2586.
IEEE DOI 2301
Speech enhancement, Feature extraction, Noise measurement, Spectrogram, Tongue, Decoding, Loss measurement, Speech synthesis, speech generation BibRef

Zhou, K.[Kun], Sisman, B.[Berrak], Rana, R.[Rajib], Schuller, B.W.[Björn W.], Li, H.Z.[Hai-Zhou],
Emotion Intensity and its Control for Emotional Voice Conversion,
AffCom(14), No. 1, January 2023, pp. 31-48.
IEEE DOI 2303
Speech recognition, Emotion recognition, Training, Speech synthesis, Hidden Markov models, Computational modeling, relative attribute BibRef

Huang, B.[Bingyuan], Cui, S.[Sanshuai], Huang, J.W.[Ji-Wu], Kang, X.[Xiangui],
Discriminative Frequency Information Learning for End-to-End Speech Anti-Spoofing,
SPLetters(30), 2023, pp. 185-189.
IEEE DOI 2303
Band-pass filters, Convolution, Task analysis, Cutoff frequency, Speech processing, Computational modeling, Robustness, ASVspoof, speech anti-spoofing BibRef

Zhao, W.[Wei], Wang, Z.[Zuyi], Xu, L.[Li],
Mandarin Text-to-Speech Front-End With Lightweight Distilled Convolution Network,
SPLetters(30), 2023, pp. 249-253.
IEEE DOI 2303
Convolution, Bit error rate, Task analysis, Kernel, Knowledge engineering, Training, Electrical engineering, convolution network BibRef

Ma, K.[Kaijie], Feng, Y.[Yifan], Chen, B.[Beijing], Zhao, G.Y.[Guo-Ying],
End-to-End Dual-Branch Network Towards Synthetic Speech Detection,
SPLetters(30), 2023, pp. 359-363.
IEEE DOI 2305
Forgery, Feature extraction, Finite element analysis, Training, Speech synthesis, Task analysis, Multitasking, ASVspoof 2019 LA, synthetic speech detection BibRef

Mira, R.[Rodrigo], Vougioukas, K.[Konstantinos], Ma, P.C.[Ping-Chuan], Petridis, S.[Stavros], Schuller, B.W.[Björn W.], Pantic, M.[Maja],
End-to-End Video-to-Speech Synthesis Using Generative Adversarial Networks,
Cyber(53), No. 6, June 2023, pp. 3454-3466.
IEEE DOI 2305
Hidden Markov models, Task analysis, Spectrogram, Visualization, Speech recognition, Predictive models, Feature extraction, video-to-speech BibRef

Yoon, H.[Hyungchan], Kim, C.[Changhwan], Um, S.[Seyun], Yoon, H.W.[Hyun-Wook], Kang, H.G.[Hong-Goo],
SC-CNN: Effective Speaker Conditioning Method for Zero-Shot Multi-Speaker Text-to-Speech Systems,
SPLetters(30), 2023, pp. 593-597.
IEEE DOI 2306
Kernel, Convolution, Convolutional neural networks, Training, Task analysis, Predictive models, Phonetics, Generalization, style transfer BibRef

Gu, Y.W.[Ye-Wei], Zhao, X.F.[Xian-Feng], Yi, X.W.[Xiao-Wei], Xiao, J.[Junchao],
Voice Conversion Using Learnable Similarity-guided Masked Autoencoder,
IWDW22(53-67).
Springer DOI 2307
BibRef

Zhang, M.Y.[Ming-Yang], Zhou, X.[Xuehao], Wu, Z.Z.[Zhi-Zheng], Li, H.Z.[Hai-Zhou],
Towards Zero-Shot Multi-Speaker Multi-Accent Text-to-Speech Synthesis,
SPLetters(30), 2023, pp. 947-951.
IEEE DOI 2308
Adaptation models, Decoding, Training, Data models, Speech synthesis, Predictive models, Standards, Accent speech synthesis, text-to-speech BibRef

Ly, E.[Edward], Villegas, J.[Julián],
Cartesian Genetic Programming Parameterization in the Context of Audio Synthesis,
SPLetters(30), 2023, pp. 1077-1081.
IEEE DOI 2309
BibRef

Mingote, V.[Victoria], Gimeno, P.[Pablo], Vicente, L.[Luis], Khurana, S.[Sameer], Laurent, A.[Antoine], Duret, J.[Jarod],
Direct Text to Speech Translation System Using Acoustic Units,
SPLetters(30), 2023, pp. 1262-1266.
IEEE DOI 2310
BibRef

Wang, Z.C.[Zhi-Chao], Chen, Y.Z.[Yuan-Zhe], Xie, L.[Lei], Tian, Q.[Qiao], Wang, Y.P.[Yu-Ping],
LM-VC: Zero-Shot Voice Conversion via Speech Generation Based on Language Models,
SPLetters(30), 2023, pp. 1157-1161.
IEEE DOI 2310
BibRef

van Niekerk, B.[Benjamin], Carbonneau, M.A.[Marc-André], Kamper, H.[Herman],
Rhythm Modeling for Voice Conversion,
SPLetters(30), 2023, pp. 1297-1301.
IEEE DOI 2310
BibRef

Zhou, K.[Kun], Sisman, B.[Berrak], Rana, R.[Rajib], Schuller, B.W.[Björn W.], Li, H.Z.[Hai-Zhou],
Speech Synthesis With Mixed Emotions,
AffCom(14), No. 4, October 2023, pp. 3120-3134.
IEEE DOI 2312
BibRef

Liu, Y.[Yan], Wei, L.F.[Li-Fang], Qian, X.Y.[Xin-Yuan], Zhang, T.H.[Tian-Hao], Chen, S.L.[Song-Lu], Yin, X.C.[Xu-Cheng],
M3TTS: Multi-modal text-to-speech of multi-scale style control for dubbing,
PRL(179), 2024, pp. 158-164.
Elsevier DOI 2403
Multi-modal text-to-speech, Memory network, Expressive speech synthesis, Multi-scale style transfer BibRef


Cong, G.X.[Gao-Xiang], Li, L.[Liang], Qi, Y.[Yuankai], Zha, Z.J.[Zheng-Jun], Wu, Q.[Qi], Wang, W.Y.[Wen-Yu], Jiang, B.[Bin], Yang, M.H.[Ming-Hsuan], Huang, Q.M.[Qing-Ming],
Learning to Dub Movies via Hierarchical Prosody Models,
CVPR23(14687-14697)
IEEE DOI 2309
BibRef

Hsu, W.N.[Wei-Ning], Remez, T.[Tal], Shi, B.[Bowen], Donley, J.[Jacob], Adi, Y.[Yossi],
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Regeneration,
CVPR23(18796-18806)
IEEE DOI 2309
BibRef

Sun, C.Z.[Cheng-Zhe], Jia, S.[Shan], Hou, S.[Shuwei], Lyu, S.W.[Si-Wei],
AI-Synthesized Voice Detection Using Neural Vocoder Artifacts,
WMF23(904-912)
IEEE DOI 2309
BibRef

Noufi, C.[Camille], May, L.[Lloyd], Berger, J.[Jonathan],
The Role of Vocal Persona in Natural and Synthesized Speech,
FG23(1-4)
IEEE DOI 2303
Human computer interaction, Face recognition, Speech recognition, Gesture recognition, Ecology, Interviews BibRef

Hwang, I.S.[In-Sun], Lee, S.H.[Sang-Hoon], Lee, S.W.[Seong-Whan],
StyleVC: Non-Parallel Voice Conversion with Adversarial Style Generalization,
ICPR22(23-30)
IEEE DOI 2212
Training, Feature extraction, Decoding BibRef

Wang, W.B.[Wen-Bin], Song, Y.[Yang], Jha, S.[Sanjay],
Autolv: Automatic Lecture Video Generator,
ICIP22(1086-1090)
IEEE DOI 2211
Measurement, Adaptation models, Synthesizers, Generators, Speech synthesis, speech synthesis, talking-head generation, e-learning BibRef

Borzě, S.[Stefano], Giudice, O.[Oliver], Stanco, F.[Filippo], Allegra, D.[Dario],
Is synthetic voice detection research going into the right direction?,
WMF22(71-80)
IEEE DOI 2210
Deep learning, Training, Image color analysis, Forensics, Conferences, Bit rate BibRef

Hassid, M.[Michael], Ramanovich, M.T.[Michelle Tadmor], Shillingford, B.[Brendan], Wang, M.[Miaosen], Jia, Y.[Ye], Remez, T.[Tal],
More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech,
CVPR22(10577-10587)
IEEE DOI 2210
Machine learning, Benchmark testing, Robustness, Pattern recognition, Synchronization, Vision + X, Vision + language BibRef

Kwak, I.Y.[Il-Youp], Kwag, S.[Sungsu], Lee, J.[Junhee], Huh, J.H.[Jun Ho], Lee, C.H.[Choong-Hoon], Jeon, Y.B.[Young-Bae], Hwang, J.H.[Jeong-Hwan], Yoon, J.W.[Ji Won],
ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map,
ICPR21(4837-4844)
IEEE DOI 2105
Deep learning, Error analysis, Transforms, Feature extraction, Complexity theory, Pattern recognition, Residual neural networks, voice presentation attack detection BibRef

Wang, D.H.[Dong-Hua], Wang, R.[Rangding], Dong, L.[Li], Yan, D.[Diqun], Ren, Y.M.[Yi-Ming],
Efficient Generation of Speech Adversarial Examples with Generative Model,
IWDW20(251-264).
Springer DOI 2103
BibRef

Zhou, H., Liu, Z., Xu, X., Luo, P., Wang, X.,
Vision-Infused Deep Audio Inpainting,
ICCV19(283-292)
IEEE DOI 2004
Code, Inpainting.
WWW Link. audio signal processing, audio-visual systems, image restoration, image segmentation, multimodality perception, BibRef

Bailer, W.[Werner], Wijnants, M.[Maarten], Lievens, H.[Hendrik], Claes, S.[Sandy],
Multimedia Analytics Challenges and Opportunities for Creating Interactive Radio Content,
MMMod20(II:375-387).
Springer DOI 2003
BibRef

Huang, T.[Ting], Wang, H.X.[Hong-Xia], Chen, Y.[Yi], He, P.S.[Pei-Song],
GRU-SVM Model for Synthetic Speech Detection,
IWDW19(115-125).
Springer DOI 2003
BibRef

Wong, A., Xu, A., Dudek, G.,
Investigating Trust Factors in Human-Robot Shared Control: Implicit Gender Bias Around Robot Voice,
CRV19(195-200)
IEEE DOI 1908
Robots, Measurement, Drones, Graphical user interfaces, Uncertainty, Psychology, trust, gender bias BibRef

Xiao, L., Wang, Z.,
Dense Convolutional Recurrent Neural Network for Generalized Speech Animation,
ICPR18(633-638)
IEEE DOI 1812
Feature extraction, Animation, Acoustics, Decoding, Visualization, Logic gates, Hidden Markov models BibRef

Shah, N.J.[Nirmesh J.], Patil, H.A.[Hemant A.],
Analysis of Features and Metrics for Alignment in Text-Dependent Voice Conversion,
PReMI17(299-307).
Springer DOI 1711
BibRef

Rybárová, R., Drozd, I., Rozinaj, G.,
GUI for interactive speech synthesis,
WSSIP16(1-4)
IEEE DOI 1608
XML BibRef

Coto-Jiménez, M.[Marvin], Goddard-Close, J.[John],
LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices,
MCPR16(280-289).
Springer DOI 1608
BibRef

Vasek, M., Rozinaj, G., Rybárová, R.,
Letter-To-Sound conversion for speech synthesizer,
WSSIP16(1-4)
IEEE DOI 1608
speech processing BibRef

Rybarová, R., del Corral, G., Rozinaj, G.,
Diphone spanish text-to-speech synthesizer,
WSSIP15(121-124)
IEEE DOI 1603
natural language processing BibRef

Verma, R., Sarkar, P., Rao, K.S.,
Conversion of neutral speech to storytelling style speech,
ICAPR15(1-6)
IEEE DOI 1511
natural language processing BibRef

Narendra, N.P., Rao, K.S.[K. Sreenivasa],
Optimal residual frame based source modeling for HMM-based speech synthesis,
ICAPR15(1-5)
IEEE DOI 1511
decision trees BibRef

Wang, Y.[Yang], Tao, J.H.[Jian-Hua], Yang, M.H.[Ming-Hao], Li, Y.[Ya],
Extended Decision Tree with or Relationship for HMM-Based Speech Synthesis,
ACPR13(225-229)
IEEE DOI 1408
decision trees BibRef

Gao, L.[Lu], Yu, H.Z.[Hong-Zhi], Zhang, J.H.[Jins-Huang], Fang, H.P.[Hua-Ping],
Research on HMM_based speech synthesis for Lhasa dialect,
IASP11(429-433).
IEEE DOI 1112
BibRef

Chakraborty, R.[Rupayan], Garain, U.[Utpal],
Role of Synthetically Generated Samples on Speech Recognition in a Resource-Scarce Language,
ICPR10(1618-1621).
IEEE DOI 1008
BibRef

Rao, K.S.[K. Sreenivasa], Maity, S.[Sudhamay], Taru, A.[Amol], Koolagudi, S.G.[Shashidhar G.],
Unit Selection Using Linguistic, Prosodic and Spectral Distance for Developing Text-to-Speech System in Hindi,
PReMI09(531-536).
Springer DOI 0912
BibRef

Bahrampour, A.[Anvar], Barkhoda, W.[Wafa], Azami, B.Z.[Bahram Zahir],
Implementation of Three Text to Speech Systems for Kurdish Language,
CIARP09(321-328).
Springer DOI 0911
BibRef

Shirbahadurkar, S.D., Bormane, D.S.,
Marathi Language Speech Synthesizer Using Concatenative Synthesis Strategy (Spoken in Maharashtra, India),
ICMV09(181-185).
IEEE DOI 0912
BibRef

Tucková, J.[Jana], Holub, J.[Jan], Dubeda, T.[Tomáš],
Technical and Phonetic Aspects of Speech Quality Assessment: The Case of Prosody Synthesis,
COST08(126-132).
Springer DOI 0810
BibRef

Bauer, D.[Dominik], Kannampuzha, J.[Jim], Kröger, B.J.[Bernd J.],
Articulatory Speech Re-synthesis: Profiting from Natural Acoustic Speech Data,
COST08(344-355).
Springer DOI 0810
BibRef

Gu, H.Y.[Hung-Yan], Cai, C.L.[Chen-Lin], Cai, S.F.[Song-Fong],
An HNM-Based Speaker-Nonspecific Timbre Transformation Scheme for Speech Synthesis,
CISP09(1-5).
IEEE DOI 0910
BibRef

Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Speaker Verification, Speaker Identification .


Last update:Mar 16, 2024 at 20:36:19