The content discusses the challenges of accurately recognizing context-dependent words and phrases in ASR systems. It introduces a novel approach using Gaussian Mixture Models, Hidden Markov Models, and Deep Neural Networks integrated with transformer-based models for better accuracy. The study focuses on lattice rescoring techniques to refine recognition results through language model integration. Experimental results show a notable improvement in transcription accuracy, especially when dealing with Out-of-Vocabulary terms.
The importance of addressing speech variability due to pronunciation, accents, and environmental factors is highlighted. The need for robust ASR systems is emphasized for applications like virtual assistants and smart home devices. The content delves into the complexities of human language understanding and the limitations of current ASR systems in context interpretation.
Technological advancements like artificial intelligence have opened new frontiers for enhancing ASR accuracy but face challenges with evolving language dynamics. Semantic lattice processing plays a crucial role in improving situational context recognition by analyzing word relationships within sentences.
The methodology involves lattice re-scoring techniques using Transformer models to refine word hypotheses based on acoustic scores and language model probabilities along paths. Detailed experimental results demonstrate a substantial reduction in Word Error Rate post-rescoring, showcasing the effectiveness of the proposed framework.
The study's implications extend to various applications like real-time transcription services, voice-controlled systems, and virtual assistants. The research aims to create more inclusive technology by improving ASR accuracy and reliability across diverse linguistic contexts.
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by Ankitha Suda... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2310.09680.pdfDeeper Inquiries