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Xie, X., Liu, L., & Jaeger, T. F. (2021-JEP:G). Cross-talker generalization in the perception of non-nativespeech: a large-scale replication ...
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Improved Spoken Language Representation for Intent Understanding in a Task-Oriented Dialogue System
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In: Sensors; Volume 22; Issue 4; Pages: 1509 (2022)
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МЕХАНИЗМЫ МЫШЛЕНИЯ И ИХ ФУНКЦИОНИРОВАНИЕ В ПРОЦЕССЕ ПЕРЕВОДЧЕСКОЙ ДЕЯТЕЛЬНОСТИ ... : MECHANISMS OF THINKING AND THEIR FUNCTIONING IN THE PROCESS OF TRANSLATION ACTIVITY ...
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Peculiarities of Children's Understanding of the Figurative Meaning of Phrases and their Use in Spontaneous Speech: The relevant a State of Development ...
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Peculiarities of Children's Understanding of the Figurative Meaning of Phrases and their Use in Spontaneous Speech: The relevant a State of Development ...
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Learning speech embeddings for speaker adaptation and speech understanding
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Modeling phones, keywords, topics and intents in spoken languages
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Psychological Approaches for Building up a Monologue-Story ; Психологічні підходи до побудови монологу-розповіді
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In: Collection of Research Papers "Problems of Modern Psychology"; No. 52 (2021): Collection of Research Papers "Problems of Modern Psychology"; 204-224 ; Збірник наукових праць "Проблеми сучасної психології"; № 52 (2021): Збірник наукових праць "Проблеми сучасної психології"; 204-224 ; 2663-6956 ; 2227-6246 ; 10.32626/2227-6246.2021-52 (2021)
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Phonemic transcription of low-resource languages: To what extent can preprocessing be automated?
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In: 1st Joint SLTU (Spoken Language Technologies for Under-resourced languages) and CCURL (Collaboration and Computing for Under-Resourced Languages) Workshop ; https://halshs.archives-ouvertes.fr/hal-02513914 ; 1st Joint SLTU (Spoken Language Technologies for Under-resourced languages) and CCURL (Collaboration and Computing for Under-Resourced Languages) Workshop, 2020, Marseille, France. pp.306-315 ; https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/SLTUCCURLbook.pdf (2020)
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Natural language understanding in argumentative dialogue systems ...
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SLURP: A Spoken Language Understanding Resource Package ...
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SLURP: A Spoken Language Understanding Resource Package ...
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Формирование умений аудирования на уровне магистратуры в МАРХИ ... : Formation of listening skills at the level of magistracy in MARHI ...
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Phonemic transcription of low-resource languages: To what extent can preprocessing be automated?
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In: 1st Joint SLTU (Spoken Language Technologies for Under-resourced languages) and CCURL (Collaboration and Computing for Under-Resourced Languages) Workshop ; https://halshs.archives-ouvertes.fr/hal-02513914 ; 1st Joint SLTU (Spoken Language Technologies for Under-resourced languages) and CCURL (Collaboration and Computing for Under-Resourced Languages) Workshop, 2020, Marseille, France. pp.306-315 ; https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/SLTUCCURLbook.pdf (2020)
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Advances in deep learning methods for speech recognition and understanding
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Communication-based Evaluation for Natural Language Generation
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
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In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02298417 ; Interspeech 2019, Sep 2019, Graz, Austria. pp.799-803, ⟨10.21437/Interspeech.2019-2732⟩ (2019)
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Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling
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In: CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing ; https://hal.inria.fr/hal-02085093 ; CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France (2019)
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Intelligibility and Listening Effort of Spanish Oesophageal Speech
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In: Applied Sciences ; Volume 9 ; Issue 16 (2019)
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Abstract:
Communication is a huge challenge for oesophageal speakers, be it for interactions with fellow humans or with digital voice assistants. We aim to quantify these communication challenges (both human&ndash ; human and human&ndash ; machine interactions) by measuring intelligibility and Listening Effort (LE) of Oesophageal Speech (OS) in comparison to Healthy Laryngeal Speech (HS). We conducted two listening tests (one web-based, the other in laboratory settings) to collect these measurements. Participants performed a sentence recognition and LE rating task in each test. Intelligibility, calculated as Word Error Rate, showed significant correlation with self-reported LE ratings. Speaker type (healthy or oesophageal) had a major effect on intelligibility and effort. More LE was reported for OS compared to HS even when OS intelligibility was close to HS. Listeners familiar with OS reported less effort when listening to OS compared to nonfamiliar listeners. However, such advantage of familiarity was not observed for intelligibility. Automatic speech recognition scores were higher for OS compared to HS.
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Keyword:
listening effort; pathological speech and language; Spanish speech; speech and voice disorders; speech intelligibility; speech perception; spoken language understanding
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URL: https://doi.org/10.3390/app9163233
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