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IEEE DOI
0401
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Elsevier DOI
0401
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Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech
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SMC-B(37), No. 5, October 2007, pp. 1305-1320.
IEEE DOI
0711
BibRef
Kay, S.,
A New Approach to Fourier Synthesis With Application to Neural Encoding
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IEEE DOI
1008
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Kay, S.,
A New Proof of the Neyman-Pearson Theorem Using the EEF and the
Vindication of Sir R. Fisher,
SPLetters(19), No. 8, August 2012, pp. 451-454.
IEEE DOI
1208
BibRef
Scanzio, S.[Stefano],
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Mana, F.[Franco],
Laface, P.,
Parallel implementation of Artificial Neural Network training for
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Elsevier DOI
1008
Artificial Neural Network; Block Back-propagation; Focused Attention
Back-Propagation; GPU; CUDA; Fast Training
BibRef
Siniscalchi, S.M.,
Yu, D.[Dong],
Deng, L.[Li],
Lee, C.H.[Chin-Hui],
Speech Recognition Using Long-Span Temporal Patterns in a Deep Network
Model,
SPLetters(20), No. 3, March 2013, pp. 201-204.
IEEE DOI
1303
BibRef
Hutchinson, B.[Brian],
Deng, L.[Li],
Yu, D.[Dong],
Tensor Deep Stacking Networks,
PAMI(35), No. 8, 2013, pp. 1944-1957.
IEEE DOI
1307
Closed-form solutions; Deep learning; handwriting image classification;
BibRef
Bengio, Y.[Yoshua],
Courville, A.[Aaron],
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Representation Learning: A Review and New Perspectives,
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IEEE DOI
Survey, Learning.
1307
Neural networks; Speech recognition; Boltzmann machine;
Deep learning; representation learning; unsupervised learning
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Swietojanski, P.,
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Convolutional Neural Networks for Distant Speech Recognition,
SPLetters(21), No. 9, September 2014, pp. 1120-1124.
IEEE DOI
1406
Acoustics
BibRef
Espi, M.[Miquel],
Fujimoto, M.[Masakiyo],
Nakatani, T.[Tomohiro],
Acoustic Event Detection in Speech Overlapping Scenarios Based on
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Richardson, F.,
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Deep Neural Network Approaches to Speaker and Language Recognition,
SPLetters(22), No. 10, October 2015, pp. 1671-1675.
IEEE DOI
1506
feature extraction
BibRef
Trentin, E.[Edmondo],
Maximum-likelihood normalization of features increases the robustness
of neural-based spoken human-computer interaction,
PRL(66), No. 1, 2015, pp. 71-80.
Elsevier DOI
1511
Feature normalization
BibRef
Lee, H.Y.,
Cho, J.W.,
Kim, M.,
Park, H.M.,
DNN-Based Feature Enhancement Using DOA-Constrained ICA for Robust
Speech Recognition,
SPLetters(23), No. 8, August 2016, pp. 1091-1095.
IEEE DOI
1608
direction-of-arrival estimation
BibRef
Sangeetha, J.,
Jothilakshmi, S.,
Automatic continuous speech recogniser for Dravidian languages using
the auto associative neural network,
IJCVR(6), No. 1-2, 2016, pp. 113-126.
DOI Link
1601
BibRef
Fredes, J.,
Novoa, J.,
King, S.,
Stern, R.M.,
Yoma, N.B.,
Locally Normalized Filter Banks Applied to Deep Neural-Network-Based
Robust Speech Recognition,
SPLetters(24), No. 4, April 2017, pp. 377-381.
IEEE DOI
1704
cepstral analysis
BibRef
Shahnawazuddin, S.,
Sinha, R.,
Pradhan, G.,
Pitch-Normalized Acoustic Features for Robust Children's Speech
Recognition,
SPLetters(24), No. 8, August 2017, pp. 1128-1132.
IEEE DOI
1708
feature extraction, spectral analysis, speech recognition,
time-frequency analysis, SMAC features,
adaptive-cepstral truncation, additive noise,
spectral smoothening approach, Additive noise,
Hidden Markov models, Mel frequency cepstral coefficient,
Robustness, Speech, Automatic speech recognition (ASR)
BibRef
Gosztolya, G.[Gábor],
Tóth, L.[László],
DNN-Based Feature Extraction for Conflict Intensity Estimation From
Speech,
SPLetters(24), No. 12, December 2017, pp. 1837-1841.
IEEE DOI
1712
estimation theory, feature extraction, greedy algorithms,
neural nets, speech processing,
feature extraction
BibRef
Gosztolya, G.[Gábor],
Bánhalmi, A.[András],
Tóth, L.[László],
Using One-Class Classification Techniques in the Anti-phoneme Problem,
IbPRIA09(433-440).
Springer DOI
0906
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Kim, M.[Minkyoung],
Kim, H.[Harksoo],
Integrated neural network model for identifying speech acts,
predicators, and sentiments of dialogue utterances,
PRL(101), No. 1, 2018, pp. 1-5.
Elsevier DOI
1801
Integrated intention identification model
BibRef
Affonso, E.T.,
Rosa, R.L.,
Rodríguez, D.Z.,
Speech Quality Assessment Over Lossy Transmission Channels Using Deep
Belief Networks,
SPLetters(25), No. 1, January 2018, pp. 70-74.
IEEE DOI
1801
IP networks, belief networks, feature extraction,
radial basis function networks, speech coding, speech processing,
speech quality assessment
BibRef
Kim, H.G.,
Lee, H.,
Kim, G.,
Oh, S.H.,
Lee, S.Y.,
Rescoring of N-Best Hypotheses Using Top-Down Selective Attention for
Automatic Speech Recognition,
SPLetters(25), No. 2, February 2018, pp. 199-203.
IEEE DOI
1802
neural nets, speech recognition,
Aurora4 speech recognition tasks,
top-down selective attention
BibRef
Kaushik, L.,
Sangwan, A.,
Hansen, J.H.L.,
Speech Activity Detection in Naturalistic Audio Environments:
Fearless Steps Apollo Corpus,
SPLetters(25), No. 9, September 2018, pp. 1290-1294.
IEEE DOI
1809
acoustic noise, acoustic signal detection, audio recording,
feedforward neural nets, learning (artificial intelligence),
speech activity detection (SAD)
BibRef
Heracleous, P.[Panikos],
Even, J.[Jani],
Sugaya, F.[Fumiaki],
Hashimoto, M.[Masayuki],
Yoneyama, A.[Akio],
Exploiting alternative acoustic sensors for improved noise robustness
in speech communication,
PRL(112), 2018, pp. 191-197.
Elsevier DOI
1809
Body-conducted sensors, Hidden Markov models (HMMs),
Automatic speech recognition, Speech intelligibility, Fusion, Noise robustness
BibRef
Takahashi, N.[Naoya],
Gygli, M.[Michael],
Van Gool, L.J.[Luc J.],
AENet: Learning Deep Audio Features for Video Analysis,
MultMed(20), No. 3, March 2018, pp. 513-524.
IEEE DOI
1802
Feature extraction, Hidden Markov models,
Mel frequency cepstral coefficient, Network architecture, Speech,
large input field
BibRef
Cho, B.J.,
Lee, J.,
Park, H.,
A Beamforming Algorithm Based on Maximum Likelihood of a Complex
Gaussian Distribution With Time-Varying Variances for Robust Speech
Recognition,
SPLetters(26), No. 9, September 2019, pp. 1398-1402.
IEEE DOI
1909
Covariance matrices, Array signal processing, Speech recognition,
Maximum likelihood estimation, Artificial neural networks,
robust speech recognition
BibRef
Gundogdu, B.,
Yusuf, B.,
Saraclar, M.,
Generative RNNs for OOV Keyword Search,
SPLetters(26), No. 1, January 2019, pp. 124-128.
IEEE DOI
1901
learning (artificial intelligence), query processing,
recurrent neural nets, search problems, speech recognition,
recurrent neural networks
BibRef
Seshadri, S.,
Räsänen, O.,
SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech,
SPLetters(26), No. 9, September 2019, pp. 1359-1363.
IEEE DOI
1909
estimation theory, learning (artificial intelligence),
natural language processing, neural net architecture,
speech processing
BibRef
Last, P.,
Engelbrecht, H.A.,
Kamper, H.,
Unsupervised Feature Learning for Speech Using Correspondence and
Siamese Networks,
SPLetters(27), 2020, pp. 421-425.
IEEE DOI
2004
Acoustics, Training, Feature extraction,
Speech processing, Standards, Data models, Unsupervised learning,
zero-resource speech processing
BibRef
John Wesley, R.,
Nayeemulla Khan, A.,
Shahina, A.,
Phoneme classification in reconstructed phase space with
convolutional neural networks,
PRL(135), 2020, pp. 299-306.
Elsevier DOI
2006
Reconstructed phase space, Time-delay embedding,
TIMIT phoneme classification, Convolutional neural network, Phase space reconstruction
BibRef
Phan, H.,
McLoughlin, I.V.,
Pham, L.,
Chén, O.Y.,
Koch, P.,
de Vos, M.,
Mertins, A.,
Improving GANs for Speech Enhancement,
SPLetters(27), 2020, pp. 1700-1704.
IEEE DOI
1806
Generators, Noise measurement, Speech enhancement,
Convolution, Decoding, Task analysis, Speech enhancement,
DSEGAN
BibRef
Wei, W.[Wei],
Wang, Z.[Zanbo],
Mao, X.L.[Xian-Ling],
Zhou, G.Y.[Guang-You],
Zhou, P.[Pan],
Jiang, S.[Sheng],
Position-aware self-attention based neural sequence labeling,
PR(110), 2021, pp. 107636.
Elsevier DOI
2011
Sequence labeling, Self-attention, Discrete context dependency
BibRef
Gu, R.Z.[Rong-Zhi],
Zhang, S.X.[Shi-Xiong],
Zou, Y.X.[Yue-Xian],
Yu, D.[Dong],
Complex Neural Spatial Filter: Enhancing Multi-Channel Target Speech
Separation in Complex Domain,
SPLetters(28), 2021, pp. 1370-1374.
IEEE DOI
2107
Customer relationship management, Spectrogram, Training,
Task analysis, Supervised learning, Speech enhancement,
MVDR
BibRef
Li, Y.X.[Yan-Xiong],
Wang, W.[Wucheng],
Liu, M.[Mingle],
Jiang, Z.J.[Zhong-Jie],
He, Q.H.[Qian-Hua],
Speaker Clustering by Co-Optimizing Deep Representation Learning and
Cluster Estimation,
MultMed(23), 2021, pp. 3377-3387.
IEEE DOI
2109
Estimation, Feature extraction, Clustering methods,
Clustering algorithms, Decoding, Neural networks,
audio document analysis
BibRef
Esmaeilpour, M.[Mohammad],
Chaalia, N.[Nourhene],
Cardinal, P.[Patrick],
RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text
Adversarial Attacks,
SPLetters(29), 2022, pp. 1998-2002.
IEEE DOI
2210
Generative adversarial networks, Training, Perturbation methods,
Signal processing algorithms, Generators, Optimization,
speech adversarial attack
BibRef
Tian, J.C.[Jin-Chuan],
Yu, J.W.[Jian-Wei],
Weng, C.[Chao],
Zou, Y.X.[Yue-Xian],
Yu, D.[Dong],
Improving Mandarin End-to-End Speech Recognition With Word N-Gram
Language Model,
SPLetters(29), 2022, pp. 812-816.
IEEE DOI
2204
Decoding, Lattices, Chaos, Artificial neural networks, Vocabulary,
Transducers, Training, Speech recognition, language model
BibRef
Mai, S.J.[Si-Jie],
Hu, H.F.[Hai-Feng],
Xing, S.L.[Song-Long],
A Unimodal Representation Learning and Recurrent Decomposition Fusion
Structure for Utterance-Level Multimodal Embedding Learning,
MultMed(24), 2022, pp. 2488-2501.
IEEE DOI
2205
Feature extraction, Logic gates, Acoustics,
Uniform resource locators, Data mining, Tensors,
recurrent decomposition fusion network
BibRef
Yang, R.[Runyan],
Cheng, G.F.[Gao-Feng],
Zhang, P.Y.[Peng-Yuan],
Yan, Y.H.[Yong-Hong],
An E2E-ASR-Based Iteratively-Trained Timestamp Estimator,
SPLetters(29), 2022, pp. 1654-1658.
IEEE DOI
2208
Training, Hidden Markov models, Acoustics, Task analysis, Decoding,
Neural networks, Automatic speech recognition, end-to-end,
text-to-speech alignment
BibRef
Muralikrishna, H.,
Aroor Dinesh, D.[Dileep],
Spoken language identification in unseen channel conditions using
modified within-sample similarity loss,
PRL(158), 2022, pp. 16-23.
Elsevier DOI
2205
Spoken language identification, Unseen channel condition,
Channel-mismatch, Domain-mismatch, Deep learning, Within-sample similarity loss
BibRef
Nasir, M.[Md],
Baucom, B.[Brian],
Bryan, C.[Craig],
Narayanan, S.[Shrikanth],
Georgiou, P.[Panayiotis],
Modeling Vocal Entrainment in Conversational Speech Using Deep
Unsupervised Learning,
AffCom(13), No. 3, July 2022, pp. 1651-1663.
IEEE DOI
2209
Medical treatment, Encoding, Feature extraction, Training,
Signal processing, Neural networks, Computational modeling,
interaction
BibRef
Lian, Z.[Zheng],
Chen, L.[Lan],
Sun, L.[Licai],
Liu, B.[Bin],
Tao, J.H.[Jian-Hua],
GCNet: Graph Completion Network for Incomplete Multimodal Learning in
Conversation,
PAMI(45), No. 7, July 2023, pp. 8419-8432.
IEEE DOI
2306
Oral communication, Correlation, Data models, Task analysis,
Feature extraction, Tensors, Benchmark testing, temporal-sensitive modeling
BibRef
Sun, H.R.[Hao-Ran],
Wang, D.[Dong],
Li, L.[Lantian],
Chen, C.[Chen],
Zheng, T.F.[Thomas F.],
Random Cycle Loss and Its Application to Voice Conversion,
PAMI(45), No. 8, August 2023, pp. 10331-10345.
IEEE DOI
2307
Codes, Speech coding, Speech recognition, Task analysis,
Probabilistic logic, Mathematical models, Analytical models,
voice conversion
BibRef
Li, L.[Linhao],
Wang, A.[Ao],
Xu, M.[Ming],
Dong, Y.F.[Yong-Feng],
Li, X.[Xin],
Abductive natural language inference by interactive model with
structural loss,
PRL(177), 2024, pp. 82-88.
Elsevier DOI
2401
Natural language inference, Abductive inference,
Deep neural network, BiLSTM, Pretrained model(RoBERTa)
BibRef
Wang, Q.Q.[Qiong-Qiong],
Lee, K.A.[Kong Aik],
Cosine Scoring With Uncertainty for Neural Speaker Embedding,
SPLetters(31), 2024, pp. 845-849.
IEEE DOI
2404
Uncertainty, Vectors, Speech recognition, Neural networks,
Measurement uncertainty, Training, Speaker recognition, uncertainty propagation
BibRef
Singh, S.[Shubhr],
Steinmetz, C.J.[Christian J.],
Benetos, E.[Emmanouil],
Phan, H.[Huy],
Stowell, D.[Dan],
ATGNN: Audio Tagging Graph Neural Network,
SPLetters(31), 2024, pp. 825-829.
IEEE DOI
2404
Spectrogram, Tagging, Correlation, Convolution, Transformers, Training,
Feature extraction, Audio tagging, graph neural networks,
computational sound scene analysis
BibRef
Wang, S.[Sijie],
Ni, L.[Lin],
Zhang, Z.[Zeyu],
Li, X.X.[Xiao-Xuan],
Zheng, X.[Xianda],
Liu, J.[Jiamou],
Multimodal prediction of student performance: A fusion of signed
graph neural networks and large language models,
PRL(181), 2024, pp. 1-8.
Elsevier DOI
2405
Signed network, Graph representations learning,
Natural language processing, Multimodal
BibRef
Song, Y.H.[Yi-Hua],
Guo, L.[Lei],
Man, M.[Menghua],
Wu, Y.[Youxi],
The spiking neural network based on fMRI for speech recognition,
PR(155), 2024, pp. 110672.
Elsevier DOI
2408
functional Magnetic Resonance Imaging,
Functional brain network, Spiking neural network,
Neural information transmission
BibRef
Ma, D.[Duo],
Yue, X.H.[Xiang-Hu],
Ao, J.[Junyi],
Gao, X.X.[Xiao-Xue],
Li, H.Z.[Hai-Zhou],
Text-Guided HuBERT: Self-Supervised Speech Pre-Training via
Generative Adversarial Networks,
SPLetters(31), 2024, pp. 2055-2059.
IEEE DOI
2408
Speech enhancement, Data models, Generative adversarial networks,
Transformers, Training, Task analysis, Phonetics, speech representation
BibRef
Kim, S.S.[Sung-Soo],
Lee, D.[Dongjune],
Kang, J.Y.[Ju Yeon],
Jeong, M.[Myeonghun],
Kim, N.S.[Nam Soo],
Sampling-Based Pruned Knowledge Distillation for Training Lightweight
RNN-T,
SPLetters(32), 2025, pp. 631-635.
IEEE DOI
2502
Lattices, Computational modeling, Training, Speech recognition,
Memory management, Complexity theory, Vectors,
speech recognition
BibRef
Aitoulghazi, O.[Omar],
Jaafari, A.[Ahmed],
Mourhir, A.[Asmaa],
DarSpeech: An Automatic Speech Recognition System for the Moroccan
Dialect,
ISCV22(1-6)
IEEE DOI
2208
Error analysis, Web and internet services,
Customer relationship management, Organizations,
Deep Speech 2
BibRef
Zhai, M.E.[Meng-En],
Dong, L.H.[Li-Hong],
Qin, Y.[Yi],
Yu, F.F.[Fei-Fan],
The Research of Chain Model Based on CNN-TDNNF in Yulin Dialect
Speech Recognition,
ICIVC22(883-888)
IEEE DOI
2301
Training, Adaptation models, Perturbation methods, Computational modeling,
Neural networks, Time series analysis, dialect speech
BibRef
Vedvyasan, K.[Kishore],
Nathwani, K.[Karan],
Hegde, R.M.[Rajesh M.],
Group Delay based Methods for Detection and Recognition of Whispered
Speech,
ICPR22(499-505)
IEEE DOI
2212
Voice activity detection, Smoothing methods, Art, Databases,
Cepstral analysis, Surveillance, Neural networks,
LP and Whisper Detection
BibRef
Toufa, A.S.[Anastasia-Sotiria],
Kotropoulos, C.[Constantine],
Digit Recognition Applied to Reconstructed Audio Signals Using Deep
Learning,
ICPR21(3050-3057)
IEEE DOI
2105
Support vector machines, Pipelines, Neural networks,
Generative adversarial networks, Audio recording, Generators,
Signal reconstruction
BibRef
Chakraborty, J.[Jaybrata],
Chakraborty, B.[Bappaditya],
Bhattacharya, U.[Ujjwal],
Dense Recognition of Spoken Languages,
ICPR21(9674-9681)
IEEE DOI
2105
Training, TV, Data preprocessing, Deep architecture,
Speech recognition, Network architecture, Linguistics
BibRef
Ghezaiel, W.[Wajdi],
Brun, L.[Luc],
LÉZORAY, O.[Olivier],
Hybrid Network For End-To-End Text-Independent Speaker Identification,
ICPR21(2352-2359)
IEEE DOI
2105
Wavelet transforms, Training, Scattering, Training data,
Speech recognition, Speaker recognition
BibRef
Zhou, P.L.[Pei-Lin],
Huang, Z.Q.[Zhi-Qi],
Liu, F.L.[Feng-Lin],
Zou, Y.X.[Yue-Xian],
PIN: A Novel Parallel Interactive Network for Spoken Language
Understanding,
ICPR21(2950-2957)
IEEE DOI
2105
Recurrent neural networks, Correlation, Fuses, Bit error rate,
Filling, Pins
BibRef
Zhu, B.L.[Bao-Luo],
Chen, X.B.[Xiao-Bing],
Chen, T.Y.[Tai-Yue],
Zhu, J.R.[Jun-Rui],
Experiment Research on Mobile Terminal Image Scene Recognition Based
on optimization,
CVIDL20(70-75)
IEEE DOI
2102
convolutional neural nets, entropy, feature extraction,
image recognition, learning (artificial intelligence),
Tensor Flow Life
BibRef
Wang, P.,
Research and Design of Smart Home Speech Recognition System Based on
Deep Learning,
CVIDL20(218-221)
IEEE DOI
2102
belief networks, feature extraction, hidden Markov models,
home automation, Internet, learning (artificial intelligence),
acoustic feature extraction
BibRef
Wang, L.,
A Speech Content Retrieval Model Based on Integrated Neural Network
for Natural Language Description,
CVIDL20(532-535)
IEEE DOI
2102
audio signal processing, content-based retrieval,
convolutional neural nets, feature extraction,
keyword embedding
BibRef
Scharenborg, O.[Odette],
van der Gouw, N.[Nikki],
Larson, M.[Martha],
Marchiori, E.[Elena],
The Representation of Speech in Deep Neural Networks,
MMMod19(II:194-205).
Springer DOI
1901
BibRef
Roth, J.,
Chaudhuri, S.,
Klejch, O.,
Marvin, R.,
Gallagher, A.,
Kaver, L.,
Ramaswamy, S.,
Stopczynski, A.,
Schmid, C.,
Xi, Z.,
Pantofaru, C.,
Supplementary Material: AVA-ActiveSpeaker:
An Audio-Visual Dataset for Active Speaker Detection,
MMVAMTC19(3718-3722)
IEEE DOI
2004
audio signal processing, audio-visual systems, face recognition,
object detection, speaker recognition, video signal processing,
neural networks
BibRef
Wang, F.,
Chen, W.,
Yang, Z.,
Xu, B.,
Self-Attention Based Network for Punctuation Restoration,
ICPR18(2803-2808)
IEEE DOI
1812
Decoding, Neural networks, Training, Encoding,
Feature extraction, Adaptation models
BibRef
Tokozume, Y.,
Ushiku, Y.,
Harada, T.,
Between-Class Learning for Image Classification,
CVPR18(5486-5494)
IEEE DOI
1812
Training, Image recognition, Standards, Speech recognition,
Data models, Neural networks, Learning systems
BibRef
Smirnov, E.,
Ivanova, E.,
Melnikov, A.,
Kalinovskiy, I.,
Oleinik, A.,
Luckyanets, E.,
Hard Example Mining with Auxiliary Embeddings,
DFW18(37-3709)
IEEE DOI
1812
Training, Prototypes, Measurement, Speech recognition,
Face recognition, Neural networks, Image recognition
BibRef
Ding, K.,
Luo, N.,
Xu, Y.,
Ke, D.,
Su, K.,
Mutual-optimization Towards Generative Adversarial Networks For
Robust Speech Recognition,
ICPR18(2699-2704)
IEEE DOI
1812
Generative adversarial networks, Generators,
Speech enhancement, Noise measurement, Speech recognition,
generative adversarial networks
BibRef
Li, C.,
Zhu, L.,
Xu, S.,
Gao, P.,
Xu, B.,
Recurrent Neural Network Based Small-footprint Wake-up-word Speech
Recognition System with a Score Calibration Method,
ICPR18(3222-3227)
IEEE DOI
1812
Dynamic programming, Feature extraction, Speech recognition,
Hidden Markov models, Real-time systems, dynamic programming search
BibRef
Li, C.,
Zhu, L.,
Xu, S.,
Gao, P.,
Xu, B.,
Compression of Acoustic Model via Knowledge Distillation and Pruning,
ICPR18(2785-2790)
IEEE DOI
1812
Computational modeling, Training, Speech recognition, Acoustics,
Neurons, Brain modeling, Recurrent neural networks, model compression
BibRef
Zhang, S.,
Liu, W.[Wen],
Qin, Y.,
Wake-up-word spotting using end-to-end deep neural network system,
ICPR16(2878-2883)
IEEE DOI
1705
Computational modeling,
Hidden Markov models, Logic gates, Neural networks,
Speech recognition, Training, CTC, LSTM, RNN, Wake-up-Word system,
speech, recognition
BibRef
Zhang, S.L.[Shi-Lei],
Qin, Y.,
Rapid feature space MLLR speaker adaptation for deep neural network
acoustic modeling,
ICPR16(2889-2894)
IEEE DOI
1705
Acoustics, Adaptation models, Data models, Hidden Markov models,
Standards, Training, Transforms, Deep Neural Networks, FMLLR,
bilinear models, rapid, speaker, adaptation
BibRef
Zheng, H.[Huadi],
Cai, W.,
Zhou, T.Y.[Tian-Yan],
Zhang, S.L.[Shi-Lei],
Li, M.,
Text-independent voice conversion using deep neural network based
phonetic level features,
ICPR16(2872-2877)
IEEE DOI
1705
Covariance matrices, Data mining, Data models, Feature extraction,
Speech, Training, Training data, Gaussian mixture model,
deep neural network, phoneme posterior probability, voice, conversion
BibRef
Zhang, B.[Bo],
Gan, Y.Q.[Yu-Qin],
Song, Y.[Yan],
Tang, B.L.[Ben-Lai],
Application of pronunciation knowledge on phoneme recognition by LSTM
neural network,
ICPR16(2906-2911)
IEEE DOI
1705
Automata, Dictionaries, Hidden Markov models, Linear programming,
Neural networks, Speech, Training,
connectionist temporal classification, phoneme recognition,
pronunciation, knowledge
BibRef
García, F.[Fernando],
Sanchis, E.[Emilio],
Hurtado, L.F.[Lluís F.],
Segarra, E.[Encarna],
Adaptive Training for Robust Spoken Language Understanding,
CIARP15(519-526).
Springer DOI
1511
BibRef
Pastor, J.[Joan],
Hurtado, L.F.[Lluís F.],
Segarra, E.[Encarna],
Sanchis, E.[Emilio],
Language Modelization and Categorization for Voice-Activated QA,
CIARP11(475-482).
Springer DOI
1111
BibRef
García, F.[Fernando],
Hurtado, L.F.[Lluís F.],
Sanchis, E.[Emilio],
Segarra, E.[Encarna],
An Active Learning Approach for Statistical Spoken Language
Understanding,
CIARP11(565-572).
Springer DOI
1111
BibRef
Hurtado, L.F.[Lluís F.],
Griol, D.[David],
Sanchis, E.[Emilio],
Segarra, E.[Encarna],
A Statistical User Simulation Technique for the Improvement of a Spoken
Dialog System,
CIARP07(743-752).
Springer DOI
0711
BibRef
Earlier: A2, A1, A4, A3:
A Dialog Management Methodology Based on Neural Networks and Its
Application to Different Domains,
CIARP08(643-650).
Springer DOI
0809
BibRef
He, H.Y.[Hai-Yan],
Wen, C.Y.[Cheng-Yi],
ART2-based multiple MLPs neural network for speaker-independent
recognition of isolated words,
ICPR92(II:590-593).
IEEE DOI
9208
BibRef
Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Speech Analysis, other than Recognition .