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Emotional Speech Recognition Using Deep Neural Networks
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In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
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In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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Text Data Augmentation for the Korean Language
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
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Emotional Speech Recognition Using Deep Neural Networks
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In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
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A Study of Data Augmentation for ASR Robustness in Low Bit Rate Contact Center Recordings Including Packet Losses
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1580 (2022)
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Modeling the effect of military oxygen masks on speech characteristics
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In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03325087 ; Interspeech 2021, Aug 2021, Brno, Czech Republic (2021)
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Simulating reading mistakes for child speech Transformer-based phone recognition
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In: Annual Conference of the International Speech Communication Association (INTERSPEECH) ; https://hal.archives-ouvertes.fr/hal-03257870 ; Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2021, Brno, Czech Republic (2021)
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A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
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| 9 |
Effekten av textaugmenteringsstrategier på träffsäkerhet, F1-värde och viktat F1-värde ; The effect of text data augmentation strategies on Accuracy, F1-score, and weighted F1-score
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| 10 |
Using Data Augmentation and Time-Scale Modification to Improve ASR of Children’s Speech in Noisy Environments
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In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
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Abstract:
Current ASR systems show poor performance in recognition of children’s speech in noisy environments because recognizers are typically trained with clean adults’ speech and therefore there are two mismatches between training and testing phases (i.e., clean speech in training vs. noisy speech in testing and adult speech in training vs. child speech in testing). This article studies methods to tackle the effects of these two mismatches in recognition of noisy children’s speech by investigating two techniques: data augmentation and time-scale modification. In the former, clean training data of adult speakers are corrupted with additive noise in order to obtain training data that better correspond to the noisy testing conditions. In the latter, the fundamental frequency (F0) and speaking rate of children’s speech are modified in the testing phase in order to reduce differences in the prosodic characteristics between the testing data of child speakers and the training data of adult speakers. A standard ASR system based on DNN–HMM was built and the effects of data augmentation, F0 modification, and speaking rate modification on word error rate (WER) were evaluated first separately and then by combining all three techniques. The experiments were conducted using children’s speech corrupted with additive noise of four different noise types in four different signal-to-noise (SNR) categories. The results show that the combination of all three techniques yielded the best ASR performance. As an example, the WER value averaged over all four noise types in the SNR category of 5 dB dropped from 32.30% to 12.09% when the baseline system, in which no data augmentation or time-scale modification were used, was replaced with a recognizer that was built using a combination of all three techniques. In summary, in recognizing noisy children’s speech with ASR systems trained with clean adult speech, considerable improvements in the recognition performance can be achieved by combining data augmentation based on noise addition in the system training phase and time-scale modification based on modifying F0 and speaking rate of children’s speech in the testing phase.
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Keyword:
data augmentation; DNN; recognition of children’s speech; time-scale modification
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URL: https://doi.org/10.3390/app11188420
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| 11 |
Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm
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In: Mathematics; Volume 10; Issue 1; Pages: 4 (2021)
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| 12 |
Volumetric changes at implant sites: A systematic appraisal of traditional methods and optical scanning- based digital technologies
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| 13 |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
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| 14 |
Improving Short Text Classification Through Global Augmentation Methods
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In: Lecture Notes in Computer Science ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) ; https://hal.inria.fr/hal-03414750 ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.385-399, ⟨10.1007/978-3-030-57321-8_21⟩ (2020)
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| 15 |
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
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In: SLT 2020 - IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070321 ; SLT 2020 - IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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Characterization and classification of semantic image-text relations ...
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Characterization and classification of semantic image-text relations ...
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| 18 |
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems
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In: Sensors ; Volume 20 ; Issue 9 (2020)
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| 19 |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
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In: Fraunhofer IAIS (2020)
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| 20 |
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
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In: Proceedings of the Society for Computation in Linguistics (2020)
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