Contextual biasing is essential for addressing scenario-specific challenges in End-to-End (E2E) Automatic Speech Recognition (ASR) systems. Prior contextual E2E ASR methods, such as the contextual bias with CPP Network, have utilized bias CTC loss for explicit supervision of bias tasks, However, the alignment between the ASR and bias tasks has been largely neglected. This paper introduces a novel contextual biasing approach that employs the Multi-label Synchronous Output CTC (MCTC) algorithm to enhance the synchronization between ASR and bias task outputs. Furthermore, we propose an enhancement to phrase-level contextual representation. Our proposed method demonstrates significant improvements in Word Error Rate (WER). Specifically, our experiments reveal a 15.0% reduction in WER on the Librispeech-960 dataset compared to the CPPN method, with an impressive 29.0% reduction in WER for context phrases.

Contextual automatic speech recognition (ASR) plays an increasingly important role in addressing the long-tail issues of general ASR. In the past, contextual ASR mainly focused on phrase-level discussions, providing a convenient way to handle biasing phrases. This paper introduces a new contextual network for extracting context tokens, with a focus on optimizing contextual knowledge using token-level information. We enhance the fusion of context information and ASR acoustic features, recognizing that textual knowledge inherently represents a distinct modality compared to acoustic information. More importantly, we propose a creative approach to address the challenge of the increasing size of expanding token list compared to phrase list. Our method is tested on the LibriSpeech and AISHELL-2 datasets, the results demonstrate a 31.0%/23.3% Word Error Rate (WER) reduction on LibriSpeech and a 26.9% reduction Character Error Rate (CER) on the named entity (NE) set from AISHELL-2.

Non-autoregressive (NAR) automatic speech recognition (ASR) models predict tokens independently and simultaneously, bringing high inference speed. However, there is still a gap in the accuracy of the NAR models compared to the autoregressive (AR) models. In this paper, we propose a single-step NAR ASR architecture with high accuracy and inference speed, called EffectiveASR. It uses an Index Mapping Vector (IMV) based alignment generator to generate alignments during training, and an alignment predictor to learn the alignments for inference. It can be trained end-to-end (E2E) with cross-entropy loss combined with alignment loss. The proposed EffectiveASR achieves competitive results on the AISHELL-1 and AISHELL-2 Mandarin benchmarks compared to the leading models. Specifically, it achieves character error rates (CER) of 4.26%/4.62% on the AISHELL-1 dev/test dataset, which outperforms the AR Conformer with about 30x inference speedup.

In automatic speech recognition (ASR) task, the output sequence should correspond to a linear transcription of the input sequence. Lots of works have been done to learn the monotonic alignment in end-to-end (E2E) ASR model, but their methods mainly focus on streaming propose and usually result in a decline in ASR performance. On the contrary, some studies have shown that for non-streaming attention-based models, monotonic alignment is beneficial to model performance. Based on this motivation, we propose the enhanced Gaussian Monotonic Alignment (e-GMA), which reduces the difficulty of learning monotonic alignment, and the reconstructed attention matrix leads to an improved accuracy in ASR tasks. Experiments on the LibriSpeech dataset demonstrate the effectiveness of the proposed approach. Comparing with a strong baseline obtained from WeNet, the proposed model yields 12.2% relative WER reduction on test-clean benchmark and 9.9% on test-other.