Automatic Speech Recognition (ASR) has witnessed a profound research
interest. Recent breakthroughs have given ASR systems different prospects such
as faithfully transcribing spoken language, which is a pivotal advancement in
building conversational agents. However, there is still an imminent challenge
of accurately discerning context-dependent words and phrases. In this work, we
propose a novel approach for enhancing contextual recognition within ASR
systems via semantic lattice processing leveraging the power of deep learning
models in accurately delivering spot-on transcriptions across a wide variety of
vocabularies and speaking styles. Our solution consists of using Hidden Markov
Models and Gaussian Mixture Models (HMM-GMM) along with Deep Neural Networks
(DNN) models integrating both language and acoustic modeling for better
accuracy. We infused our network with the use of a transformer-based model to
properly rescore the word lattice achieving remarkable capabilities with a
palpable reduction in Word Error Rate (WER). We demonstrate the effectiveness
of our proposed framework on the LibriSpeech dataset with empirical analyses.

我们提出了一种新方法，在自动语音识别系统中通过语义格处理利用深度学习模型来提高上下文识别的能力，从而准确地递交准确的转录结果，涵盖广泛的词汇和说话风格。我们在隐藏马尔可夫模型和高斯混合模型以及深度神经网络模型上融合语言和声学建模，通过使用基于 Transformer 的模型对单词格进行重新评分，实现了显著的性能提升和词错误率的明显降低，在 LibriSpeech 数据集上进行了实证分析，证明了我们提出的框架的有效性。