task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | CNN | https://arxiv.org/abs/2110.03427v3 | Accuracy | 0.948 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.91 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (No Noise) | CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.91 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Crackling Noise) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.93 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Crackling Noise) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.93 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Crackling Noise) | CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.83 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (Crackling Noise) | CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.82 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge European | 2D ConvNet(MixUp=YES) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 96.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge European | 2D ConvNet(MixUp=NO) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 96.0 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge European | 2D ConvNet with Attention and GRU(MixUp=NO) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 94.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge European | 1D ConvNet(MixUp=NO) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 94.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge European | 2D ConvNet with Attention and GRU(MixUp=YES) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 93.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | IndicTTS | CRNN | https://arxiv.org/abs/2110.03427v3 | Classification Accuracy | 0.987 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | IndicTTS | CRNN Attention | https://arxiv.org/abs/2110.03427v3 | Classification Accuracy | 0.987 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | IndicTTS | CNN | https://arxiv.org/abs/2110.03427v3 | Classification Accuracy | 0.983 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-LDE | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 8.25 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-LDE | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 2.61 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-LDE | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 1.16 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-LDE | https://arxiv.org/pdf/2011.12998.pdf | Average | 4.00 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-SAP | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 8.59 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-SAP | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 2.49 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-SAP | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 1.09 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | CNN-SAP | https://arxiv.org/pdf/2011.12998.pdf | Average | 4.06 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (cleaned data) | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 9.39 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (cleaned data) | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 3.14 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (cleaned data) | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 1.90 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (cleaned data) | https://arxiv.org/pdf/2011.12998.pdf | Average | 4.81 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (noisy data) | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 10.58 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (noisy data) | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 3.33 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (noisy data) | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 1.72 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Resnet34 (noisy data) | https://arxiv.org/pdf/2011.12998.pdf | Average | 5.21 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Fusion of models | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 15.29 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Fusion of models | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 4.54 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Fusion of models | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 1.30 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Fusion of models | https://arxiv.org/pdf/2011.12998.pdf | Average | 7.04 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | GMM-MMI | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 17.28 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | GMM-MMI | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 5.90 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | GMM-MMI | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 2.10 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | GMM-MMI | https://arxiv.org/pdf/2011.12998.pdf | Average | 8.42 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Phonotactic | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 18.59 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Phonotactic | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 6.28 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Phonotactic | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 1.34 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Phonotactic | https://arxiv.org/pdf/2011.12998.pdf | Average | 8.73 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector DNN | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 19.67 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector DNN | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 7.84 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector DNN | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 3.31 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector DNN | https://arxiv.org/pdf/2011.12998.pdf | Average | 10.27 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector | https://arxiv.org/pdf/2011.12998.pdf | 3 sec | 26.04 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector | https://arxiv.org/pdf/2011.12998.pdf | 10 sec | 11.93 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector | https://arxiv.org/pdf/2011.12998.pdf | 30 sec | 4.52 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | LRE07 | Kaldi i-vector | https://arxiv.org/pdf/2011.12998.pdf | Average | 14.17 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | SVM | http://arxiv.org/abs/1509.06928v2 | ACC | 45.2% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | SVM | http://arxiv.org/abs/1509.06928v2 | PRC | 44.8% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | SVM | http://arxiv.org/abs/1509.06928v2 | RCL | 45.4% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | n-gram Language Model | http://arxiv.org/abs/1509.06928v2 | ACC | 40.4% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | n-gram Language Model | http://arxiv.org/abs/1509.06928v2 | PRC | 40.2% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | n-gram Language Model | http://arxiv.org/abs/1509.06928v2 | RCL | 41.3% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | Max Ent | http://arxiv.org/abs/1509.06928v2 | ACC | 40% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | Max Ent | http://arxiv.org/abs/1509.06928v2 | PRC | 40% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | Max Ent | http://arxiv.org/abs/1509.06928v2 | RCL | 40.6% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | Naive Bayes | http://arxiv.org/abs/1509.06928v2 | ACC | 37.9% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | Naive Bayes | http://arxiv.org/abs/1509.06928v2 | PRC | 37.5% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | Untranscribed mixed-speech dataset | Naive Bayes | http://arxiv.org/abs/1509.06928v2 | RCL | 50.2% |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge Commonwealth | 2D ConvNet(MixUp=YES) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 95.4 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge Commonwealth | 2D ConvNet with Attention and GRU(MixUp=YES) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 95.0 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge Commonwealth | 2D ConvNet(MixUp=NO) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 94.3 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge Commonwealth | 1D ConvNet(MixUp=NO) | https://arxiv.org/abs/1910.04269v1 | Accuracy (%) | 93.7 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | CRNN | https://arxiv.org/abs/2110.03427v3 | Accuracy | 0.912 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.91 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | Inception-v3 CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.91 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | CRNN Attention | https://arxiv.org/abs/2110.03427v3 | Accuracy | 0.888 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | CNN | https://arxiv.org/abs/2110.03427v3 | Accuracy | 0.871 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | CRNN | http://arxiv.org/abs/1708.04811v1 | F1 Score | 0.63 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | YouTube News dataset (White Noise) | CRNN | http://arxiv.org/abs/1708.04811v1 | Accuracy | 0.63 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the automatically filtered (cleaned) data | https://arxiv.org/pdf/2011.12998.pdf | PC | 0.041 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the automatically filtered (cleaned) data | https://arxiv.org/pdf/2011.12998.pdf | PO | 0.056 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the automatically filtered (cleaned) data | https://arxiv.org/pdf/2011.12998.pdf | EC | 0.022 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the automatically filtered (cleaned) data | https://arxiv.org/pdf/2011.12998.pdf | EO | 0.058 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the noisy data | https://arxiv.org/pdf/2011.12998.pdf | PC | 0.055 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the noisy data | https://arxiv.org/pdf/2011.12998.pdf | PO | 0.083 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the noisy data | https://arxiv.org/pdf/2011.12998.pdf | EC | 0.033 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | KALAKA-3 | Model on the noisy data | https://arxiv.org/pdf/2011.12998.pdf | EO | 0.059 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge | LEAF | https://arxiv.org/abs/2207.05508v1 | Accuracy | 91.5 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge | EfficientLEAF | https://arxiv.org/abs/2207.05508v1 | Accuracy | 86.6 |
Dialogue > Dialogue Understanding > Spoken Language Understanding > Spoken language identification | VoxForge | melspect | https://arxiv.org/abs/2207.05508v1 | Accuracy | 85.6 |
Dialogue > Dialogue Understanding > Dialogue Safety Prediction | rt-inod-jailbreaking | Baseline | https://arxiv.org/abs/2404.09785v1 | Best-of | 0.92 |
Dialogue > Dialogue Understanding > Dialogue Safety Prediction | rt-inod-jailbreaking | GPT-4 | https://arxiv.org/abs/2404.09785v1 | Best-of | 0.91 |
Dialogue > Dialogue Understanding > Dialogue Safety Prediction | rt-inod-jailbreaking | Gemma | https://arxiv.org/abs/2404.09785v1 | Best-of | 0.91 |
Dialogue > Dialogue Understanding > Dialogue Safety Prediction | rt-inod-jailbreaking | Mistral | https://arxiv.org/abs/2404.09785v1 | Best-of | 0.87 |
Dialogue > Dialogue Understanding > Dialogue Safety Prediction | rt-inod-jailbreaking | Llama2 | https://arxiv.org/abs/2404.09785v1 | Best-of | 0.86 |
Dialogue > Dialogue Understanding > Dialogue Safety Prediction | ProsocialDialog | Canary | https://arxiv.org/abs/2205.12688v2 | Accuracy | 77.08 |
Dialogue > Empathetic Response Generation | EmpatheticDialogues | Emotion-aware transformer encoder (Transformer-XL) | null | BLEU | 0.225 |
Dialogue > Empathetic Response Generation | EmpatheticDialogues | Emotion-aware transformer encoder (Transformer-XL) | https://arxiv.org/abs/2204.11320v1 | BLEU | 0.225 |
Dialogue > Goal-Oriented Dialog | Kvret | IJEEL-KVL | https://arxiv.org/abs/2001.10468v1 | BLEU | 0.1831 |
Dialogue > Goal-Oriented Dialog | Kvret | IJEEL-KVL | https://arxiv.org/abs/2001.10468v1 | Embedding Average | 95.5 |
Dialogue > Goal-Oriented Dialog | Kvret | IJEEL-KVL | https://arxiv.org/abs/2001.10468v1 | Vector Extrema | 97.4 |
Dialogue > Goal-Oriented Dialog | Kvret | IJEEL-KVL | https://arxiv.org/abs/2001.10468v1 | Greedy Matching | 62.5 |
Dialogue > Dialogue Act Classification | EMOTyDA | Hierarchical Fusion | https://aclanthology.org/2023.findings-emnlp.505.pdf | Accuracy | 63.42 |
Dialogue > Dialogue Act Classification | Switchboard corpus | HGRU + Beam Search + Guided attention | https://arxiv.org/abs/2002.08801v2 | Accuracy | 85.0 |
Dialogue > Dialogue Act Classification | Switchboard corpus | Speaker | https://arxiv.org/abs/2109.05056v1 | Accuracy | 83.2 |
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