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.

翻译：自动语音识别（ASR）领域引发了广泛的研究兴趣。近期突破为ASR系统带来了不同前景，例如忠实转录口语这一构建对话智能体的关键进展。然而，准确识别上下文相关词汇和短语仍是亟待解决的挑战。本研究提出了一种新颖方法，通过语义格栅处理增强ASR系统的上下文识别能力，借助深度学习模型在广泛词汇和说话风格中精确实现精准转录。我们的解决方案采用隐马尔可夫模型与高斯混合模型（HMM-GMM），结合集成语言和声学建模的深度神经网络（DNN）模型以提高准确性。在网络中融入基于Transformer的模型对词格栅进行重评分，实现了显著性能提升，并切实降低了词错误率（WER）。通过LibriSpeech数据集的实证分析，我们验证了所提出框架的有效性。