Contextual biasing refers to the problem of biasing the automatic speech
recognition (ASR) systems towards rare entities that are relevant to the
specific user or application scenarios. We propose algorithms for contextual
biasing based on the Knuth-Morris-Pratt algorithm for pattern matching. During
beam search, we boost the score of a token extension if it extends matching
into a set of biasing phrases. Our method simulates the classical approaches
often implemented in the weighted finite state transducer (WFST) framework, but
avoids the FST language altogether, with careful considerations on memory
footprint and efficiency on tensor processing units (TPUs) by vectorization.
Without introducing additional model parameters, our method achieves
significant word error rate (WER) reductions on biasing test sets by itself,
and yields further performance gain when combined with a model-based biasing
method.

基于 Knuth-Morris-Pratt 算法的上下文偏置方法通过在束搜索期间提升匹配到一组偏置短语的令牌扩展的得分来解决偏向稀有实体的自动语音识别系统的问题，在不引入额外模型参数的情况下，本方法自身在偏置测试集上实现了显著的词错误率的降低，并在与基于模型的偏置方法相结合时获得了进一步的性能提升。

使用 Knuth-Morris-Pratt 匹配算法的上下文偏差

Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

Prompts are crucial to large language models as they provide context
information such as topic or logical relationships. Inspired by this, we
propose PromptASR, a framework that integrates prompts in end-to-end automatic
speech recognition (E2E ASR) systems to achieve contextualized ASR with
controllable style of transcriptions. Specifically, a dedicated text encoder
encodes the text prompts and the encodings are injected into the speech encoder
by cross-attending the features from two modalities. When using the ground
truth text from preceding utterances as content prompt, the proposed system
achieves 21.9% and 6.8% relative word error rate reductions on a book reading
dataset and an in-house dataset compared to a baseline ASR system. The system
can also take word-level biasing lists as prompt to improve recognition
accuracy on rare words. An additional style prompt can be given to the text
encoder and guide the ASR system to output different styles of transcriptions.
The code is available at icefall.

使用提示将上下文信息以控制风格的方式整合到端到端自动语音识别系统中，从而提高识别准确性和降低词错误率。

具有可控风格的上下文感知语音识别的 PromptASR

PromptASR for contextualized ASR with controllable style

Speech representations learned in a self-supervised fashion from massive
unlabeled speech corpora have been adapted successfully toward several
downstream tasks. However, such representations may be skewed toward canonical
data characteristics of such corpora and perform poorly on atypical, non-native
accented speaker populations. With the state-of-the-art HuBERT model as a
baseline, we propose and investigate self-supervised adaptation of speech
representations to such populations in a parameter-efficient way via training
accent-specific residual adapters. We experiment with 4 accents and choose
automatic speech recognition (ASR) as the downstream task of interest. We
obtain strong word error rate reductions (WERR) over HuBERT-large for all 4
accents, with a mean WERR of 22.7% with accent-specific adapters and a mean
WERR of 25.1% if the entire encoder is accent-adapted. While our experiments
utilize HuBERT and ASR as the downstream task, our proposed approach is both
model and task-agnostic.

该论文提出了一种自我监督适应口音特定的噪音样本以提高自动语音识别的识别精度的方法，并在 4 个口音种类上获得了显著的词错误率降低。

通过剩余适配器的语音表示的口音适应

Don't Stop Self-Supervision: Accent Adaptation of Speech Representations  via Residual Adapters

Rich sources of variability in natural speech present significant challenges
to current data intensive speech recognition technologies. To model both
speaker and environment level diversity, this paper proposes a novel Bayesian
factorised speaker-environment adaptive training and test time adaptation
approach for Conformer ASR models. Speaker and environment level
characteristics are separately modeled using compact hidden output transforms,
which are then linearly or hierarchically combined to represent any
speaker-environment combination. Bayesian learning is further utilized to model
the adaptation parameter uncertainty. Experiments on the 300-hr WHAM noise
corrupted Switchboard data suggest that factorised adaptation consistently
outperforms the baseline and speaker label only adapted Conformers by up to
3.1% absolute (10.4% relative) word error rate reductions. Further analysis
shows the proposed method offers potential for rapid adaption to unseen
speaker-environment conditions.

本文提出了一种新的基于贝叶斯因式分解的说话人和环境自适应训练和测试方法，用于 Conformer ASR 模型，实验结果表明，该方法在 WHAM 噪声下的 Switchboard 数据中优于基线模型和仅适配说话人标签的 Conformer 模型，可以实现快速适应无见说话人和环境条件。