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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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| 4 |
Emotional Speech Recognition Using Deep Neural Networks
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In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
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| 5 |
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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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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Abstract:
Att utveckla en sofistikerad chatbotlösning kräver stora mängder textdata för att kunna anpassalösningen till en specifik domän. Att manuellt skapa en komplett uppsättning textdata, specialanpassat för den givna domänen och innehållandes ett stort antal varierande meningar som en människa kan tänkas yttra, är ett enormt tidskrävande arbete. För att kringgå detta tillämpas dataaugmentering för att generera mer data utifrån en mindre uppsättning redan existerande textdata. Softronic AB vill undersöka alternativa strategier för dataaugmentering med målet att eventuellt ersätta den nuvarande lösningen med en mer vetenskapligt underbyggd sådan. I detta examensarbete har prototypmodeller utvecklats för att jämföra och utvärdera effekten av olika textaugmenteringsstrategier. Resultatet av genomförda experiment med prototypmodellerna visar att augmentering genom synonymutbyten med en domänanpassad synonymordlista, presenterade märkbart förbättrade effekter på förmågan hos en NLU-modell att korrekt klassificera data, gentemot övriga utvärderade strategier. Vidare indikerar resultatet att ett samband föreligger mellan den strukturella variationsgraden av det augmenterade datat och de tillämpade språkparens semantiska likhetsgrad under tillbakaöversättningar. ; Developing a sophisticated chatbot solution requires large amounts of text data to be able to adapt the solution to a specific domain. Manually creating a complete set of text data, specially adapted for the given domain, and containing a large number of varying sentences that a human conceivably can express, is an exceptionally time-consuming task. To circumvent this, data augmentation is applied to generate more data based on a smaller set of already existing text data. Softronic AB wants to investigate alternative strategies for data augmentation with the aim of possibly replacing the current solution with a more scientifically substantiated one. In this thesis, prototype models have been developed to compare and evaluate the effect of different text augmentation strategies. The results of conducted experiments with the prototype models show that augmentation through synonym swaps with a domain-adapted thesaurus, presented noticeably improved effects on the ability of an NLU-model to correctly classify data, compared to other evaluated strategies. Furthermore, the result indicates that there is a relationship between the structural degree of variation of the augmented data and the applied language pair's semantic degree of similarity during back-translations.
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Keyword:
back translation; brusinjektion; F1-score; F1-värde; Language Technology (Computational Linguistics); noise injection; RASA NLU; Språkteknologi (språkvetenskaplig databehandling); synonym swap; synonymutbyte; Text data augmentation; Textdataaugmentering; tillbakaöversättning
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URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296550
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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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| 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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| 17 |
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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