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Smart Auto-completion in Live Chat Utilizing the Power of T5 ; Smart automatisk komplettering i livechatt som utnyttjar styrkan hos T5
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Wang, Zhanpeng. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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Sequence-to-Sequence Acoustic Modeling with Semi-Stepwise Monotonic Attention for Speech Synthesis
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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Improving Grapheme-to-Phoneme Conversion for Anglicisms in German Speech Recognition
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In: Fraunhofer IAIS (2021)
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Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders
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Asm2Seq: Explainable Assembly Code Functional Summary Generation
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The Rare Word Issue in Natural Language Generation: A Character-Based Solution
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Controllable Sentence Simplification
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678214 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
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In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
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Tamil Paraphrase Detection Using Encoder-Decoder Neural Networks
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In: IFIP Advances in Information and Communication Technology ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS) ; https://hal.inria.fr/hal-03434784 ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.30-42, ⟨10.1007/978-3-030-63467-4_3⟩ (2020)
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A Data-Efficient End-to-End Spoken Language Understanding Architecture
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In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP) ; https://hal.archives-ouvertes.fr/hal-03094850 ; International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020, Barcellone, Spain (2020)
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Comparison of Word2vec with Hash2vec for Machine Translation
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In: Master's Projects (2020)
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Advances in deep learning methods for speech recognition and understanding
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Interpreting Sequence-to-Sequence Models for Russian Inflectional Morphology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
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In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
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SEQUENCE-TO-SEQUENCE MODELLING OF F0 FOR SPEECH EMOTION CONVERSION
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In: IEEE International Conference on Acoustics, Speech, and Signal Processing ; https://hal.sorbonne-universite.fr/hal-02018439 ; IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2019, Brighton, United Kingdom (2019)
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Abstract:
International audience ; Voice interfaces are becoming wildly popular and driving demand for more advanced speech synthesis and voice transformation systems. Current text-to-speech methods produce realistic sounding voices, but they lack the emotional expressivity that listeners expect , given the context of the interaction and the phrase being spoken. Emotional voice conversion is a research domain concerned with generating expressive speech from neutral synthesised speech or natural human voice. This research investigated the effectiveness of using a sequence-to-sequence (seq2seq) encoder-decoder based model to transform the intonation of a human voice from neutral to expressive speech, with some preliminary introduction of linguistic conditioning. A subjective experiment conducted on the task of speech emotion recognition by listeners successfully demonstrated the effectiveness of the proposed sequence-to-sequence models to produce convincing voice emotion transformations. In particular, conditioning the model on the position of the syllable in the phrase significantly improved recognition rates.
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Keyword:
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; intonation; Sequence-to-sequence models; Speech emotion conversion
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URL: https://hal.sorbonne-universite.fr/hal-02018439 https://hal.sorbonne-universite.fr/hal-02018439/document https://hal.sorbonne-universite.fr/hal-02018439/file/Voice_Emotion_Conversion%281%29.pdf
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Abstractive Sentence Compression with Event Attention
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In: Applied Sciences ; Volume 9 ; Issue 19 (2019)
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Diverse Decoding for Abstractive Document Summarization
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In: Applied Sciences ; Volume 9 ; Issue 3 (2019)
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Neural Sign Language Translation Based on Human Keypoint Estimation
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In: Applied Sciences ; Volume 9 ; Issue 13 (2019)
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Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding
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