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akaturk Akademik ölçüm

OpenAlex konusu

Speech Recognition and Synthesis

Bu sayfa OpenAlex konu etiketine göre çalışmaları ve o konuda görünen akademisyenleri listeler. YÖKSİS temel alan / yan dal değildir.

OpenAlex 1.072 eser 73 yazar konusu

Çalışmalar

1.072 eser

  1. YÖKSİS SJR Q1 JCR Q2 OpenAlex üst %1 OpenAlex 100.0%

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  2. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %1 OpenAlex 99.7%

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  3. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %1 OpenAlex 99.7%

    Özet henüz yok.

  4. OpenAlex üst %1 OpenAlex 99.6%

    An increasing number of independent studies have con-firmed the vulnerability of automatic speaker verification (ASV) technology to spoofing. However, in comparison to that involving other biometric modalities, spoofing and countermea-sure research for ASV is still in its infancy. A current barrier to progress is the…

  5. OpenAlex üst %1 OpenAlex 99.2%

    The performance of biometric systems based on automatic speaker recognition technology is severely degraded due to spoofing attacks with synthetic speech generated using different voice conversion (VC) and speech synthesis (SS) techniques. Various countermeasures are proposed to detect this type of at-tack, and in thi…

  6. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %1 OpenAlex 99.5%

    Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing counterm…

  7. OpenAlex üst %1 OpenAlex 99.1%

    Recent work on spoken document retrieval has suggested that it is adequate to take the singlebest output of ASR, and perform text retrieval on this output. This is reasonable enough for the task of retrieving broadcast news stories, where word error rates are relatively low, and the stories are long enough to contain…

  8. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 97.7%

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  9. OpenAlex üst %1 OpenAlex 99.8%

    Deep learning can be used for audio signal classification in a variety of ways. It can be used to detect and classify various types of audio signals such as speech, music, and environmental sounds. Deep learning models are able to learn complex patterns of audio signals and can be trained on large datasets to achieve…

  10. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 97.0%

    It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification us…

  11. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 97.0%

    It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification us…

  12. YÖKSİS SJR Q1 JCR Q1 OpenAlex üst %10 OpenAlex 97.0%

    It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification us…

Akademisyenler

73 akademisyen