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Advanced Fusion-Based Speech Emotion Recognition System Using a Dual-Attention Mechanism with Conv-Caps and Bi-GRU Features
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In: Electronics; Volume 11; Issue 9; Pages: 1328 (2022)
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42 |
Automatic Speech Recognition (ASR) Systems for Children: A Systematic Literature Review
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4419 (2022)
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A Rule-Based Grapheme-to-Phoneme Conversion System
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2758 (2022)
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Abstract:
This article presents a rule-based grapheme-to-phoneme conversion method and algorithm for Polish. It should be noted that the fundamental grapheme-to-phoneme conversion rules have been developed by Maria Steffen-Batóg and presented in her set of monographs dedicated to the automatic grapheme-to-phoneme conversion of texts in Polish. The author used previously developed rules and independently developed the grapheme-to-phoneme conversion algorithm.The algorithm has been implemented as a software application called TransFon, which allows the user to convert any text in Polish orthography to corresponding strings of phonemes, in phonemic transcription. Using TransFon, a phonemic Polish language corpus was created out of an orthographic corpus. The phonemic language corpusallows statistical analysis of the Polish language, as well as the development of phoneme- and word-based language models for automatic speech recognition using statistical methods. The developed phonemic language corpus opens up further opportunities for research to improve automatic speech recognition in Polish. The development of statistical methods for speech recognition and language modelling requires access to large language corpora, including phonemic corpora. The method presented here enables the creation of such corpora.
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Keyword:
grapheme-to-phoneme conversion; language corpus; language modelling; language statistical analysis; speech recognition
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URL: https://doi.org/10.3390/app12052758
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44 |
The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning
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In: Sensors; Volume 22; Issue 7; Pages: 2461 (2022)
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Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition
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In: Sensors; Volume 22; Issue 1; Pages: 321 (2022)
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An Empirical Performance Analysis of the Speak Correct Computerized Interface
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In: Processes; Volume 10; Issue 3; Pages: 487 (2022)
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Influence of Highly Inflected Word Forms and Acoustic Background on the Robustness of Automatic Speech Recognition for Human–Computer Interaction
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In: Mathematics; Volume 10; Issue 5; Pages: 711 (2022)
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Research on Speech Emotion Recognition Based on AA-CBGRU Network
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In: Electronics; Volume 11; Issue 9; Pages: 1409 (2022)
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49 |
Delayed auditory feedback elicits serial order errors (Malloy et al., 2022) ...
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Delayed auditory feedback elicits serial order errors (Malloy et al., 2022) ...
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Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)
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In: https://halshs.archives-ouvertes.fr/halshs-03647315 ; 2022 (2022)
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Technology in audiovisual translation practices and training ; Las tecnologías en la formación y las prácticas de traducción audiovisual
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In: CLINA Revista Interdisciplinaria de Traducción Interpretación y Comunicación Intercultural; Vol. 7 Núm. 1 (2021); 17-24 ; CLINA Revista Interdisciplinaria de Traducción Interpretación y Comunicación Intercultural; Vol. 7 No. 1 (2021); 17-24 ; 2444-1961 ; 10.14201/clina202171 (2022)
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Semi-supervised cycle-consistency training for end-to-end ASR using unpaired speech
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Using acoustic distance and acoustic absement to quantify lexical competition
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Detection and Recognition of Asynchronous Auditory/Visual Speech: Effects of Age, Hearing Loss, and Talker Accent
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Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice
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In: EUSIPCO 2021 ; https://hal.archives-ouvertes.fr/hal-03324033 ; EUSIPCO 2021, Aug 2021, Dublin (en ligne), Ireland. ⟨10.23919/EUSIPCO54536.2021.9616299⟩ (2021)
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Automatic Speech Recognition systems errors for objective sleepiness detection through voice
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In: Proceedings Interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03328827 ; Interspeech 2021, Aug 2021, Brno (virtual), Czech Republic. pp.2476-2480, ⟨10.21437/Interspeech.2021-291⟩ (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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In: INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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