Speech Recognition Error

Speech recognition error research focuses on improving the accuracy of automatic speech recognition (ASR) systems and mitigating the impact of errors on downstream tasks. Current research emphasizes developing more robust error prediction models, often employing neural sequence-to-sequence architectures or incorporating contextual information to reduce errors, particularly for low-frequency words. These advancements are crucial for enhancing the reliability of speech-based applications across diverse fields, from improving natural language processing systems to enabling more accurate clinical diagnoses based on speech analysis. The ultimate goal is to create ASR systems that are more resilient to noise and variations in speech, leading to more accurate and reliable applications.