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Models/CRNN Attention

CRNN Attention

Reported on 9 benchmarks across 3 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Speech6 results

  • DialogueonYouTube News dataset (No Noise)
    Accuracy · 2021-10-05
    0.966
    best: 0.967 (CRNN)
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • DialogueonIndicTTS
    Classification Accuracy· 2021-10-05
    0.987
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • DialogueonYouTube News dataset (White Noise)
    Accuracy · 2021-10-05
    0.888
    best: 0.912 (CRNN)
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • Spoken Language UnderstandingonYouTube News dataset (No Noise)
    Accuracy · 2021-10-05
    0.966
    best: 0.967 (CRNN)
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • Spoken Language UnderstandingonIndicTTS
    Classification Accuracy· 2021-10-05
    0.987
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • Spoken Language UnderstandingonYouTube News dataset (White Noise)
    Accuracy · 2021-10-05
    0.888
    best: 0.912 (CRNN)
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427

Natural Language Processing3 results

  • Dialogue UnderstandingonYouTube News dataset (No Noise)
    Accuracy · 2021-10-05
    0.966
    best: 0.967 (CRNN)
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • Dialogue UnderstandingonIndicTTS
    Classification Accuracy· 2021-10-05
    0.987
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427
  • Dialogue UnderstandingonYouTube News dataset (White Noise)
    Accuracy · 2021-10-05
    0.888
    best: 0.912 (CRNN)
    Is Attention always needed? A Case Study on Language Identification from SpeecharXiv:2110.03427