Papers with Librispeech dataset
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm (2021.eacl-main)
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| Challenge: | Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) a higher WER means a lower percentage of errors between the ground truth and the transcription of the system. |
| Approach: | They propose a new balanced paradigm for automatic Word Error Rate estimation using a Librispeech dataset and a Google Cloud's Speech-to-Text API. |
| Outcome: | The proposed approach is more effective than regression in a classification setting, but suffers from heavy class imbalance. |
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition (2022.findings-emnlp)
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| Challenge: | Recent studies have shown that streaming end-to-end speech recognition models suffer from higher word error rates (WER) compared to non-streaming models, streaming endto-ended ASR models are limited to short audio context or not use future context to satisfy low latency constraints. |
| Approach: | They propose a 2nd-pass rescoring model on top of the 1st-pass streaming model to improve recognition accuracy while keeping latency low. |
| Outcome: | The proposed method improves word error rate significantly compared to the existing model without adding any additional parameters or latency. |
A Unified Speaker Adaptation Approach for ASR (2021.emnlp-main)
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| Challenge: | Adapting a model to target speakers requires a lot of compute and may cause catastrophic forgetting to the existing speakers. |
| Approach: | They propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation. |
| Outcome: | The proposed model outperforms baseline models with 20.58% relative WER reduction and surpasses finetuning method by 2.54% on target speaker adaptation. |