End-to-end ASR model with speaker-specific cross-attention for two-party conversations outperforms standard models on the Switchboard corpus.
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10\% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Self multi-head attention applied after CNN encoding of spectrograms outperforms temporal and statistical pooling for speaker verification on VoxCeleb1 with 18% relative EER reduction.
citing papers explorer
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Cross-Attention End-to-End ASR for Two-Party Conversations
End-to-end ASR model with speaker-specific cross-attention for two-party conversations outperforms standard models on the Switchboard corpus.
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Self Multi-Head Attention for Speaker Recognition
Self multi-head attention applied after CNN encoding of spectrograms outperforms temporal and statistical pooling for speaker verification on VoxCeleb1 with 18% relative EER reduction.