While the end-to-end speech recognition models show impressive performance on many domains, they have difficulties in decoding long-form utterances. The overlapped inference algorithm with tie-breaking between two parallel hypotheses has been proposed for long-form speech recognition and shows dramatic performance improvements at the expense of double computational costs. In this paper, we propose a more effective way of overlapped inference by aligning partially matched hypotheses. Through the experiment on LibriSpeech dataset, the proposed algorithm showed improved performance with less computational cost compared to the conventional overlapped inference.
We propose an adapter based multi-domain Transformer based language model (LM) for Transformer ASR. The model consists of a big size common LM and small size adapters. The model can perform multi-domain adaptation with only the small size adapters and its related layers. The proposed model can reuse the full finetuned LM which is fine-tuned using all layers of an original model. The proposed LM can be expanded to new domains by adding about 2% of parameters for a first domain and 13% parameters for after second domain. The proposed model is also effective in reducing the model maintenance cost because it is possible to omit the costly and time-consuming common LM pre-training process. Using proposed adapter based approach, we observed that a general LM with adapter can outperform a dedicated music domain LM in terms of word error rate (WER).
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