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Found 4 papers, 1 papers with code
MAD: Meta Adversarial Defense Benchmark
In addition, we introduce a meta-learning based adversarial training (Meta-AT) algorithm as the baseline, which features high robustness to unseen adversarial attacks through few-shot learning.
Transformer Language Models with LSTM-based Cross-utterance Information Representation
To encode the cross-utterance information, the R-TLM incorporates an LSTM module together with a segment-wise recurrence in some of the Transformer blocks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Content-Aware Speaker Embeddings for Speaker Diarisation
Recent speaker diarisation systems often convert variable length speech segments into fixed-length vector representations for speaker clustering, which are known as speaker embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Cross-Utterance Language Models with Acoustic Error Sampling
A cross-utterance LM (CULM) is proposed in this paper, which augments the input to a standard long short-term memory (LSTM) LM with a context vector derived from past and future utterances using an extraction network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
