1) Speech recognition systems often have lower accuracy in real-world applications compared to experimental settings due to noise, distortions, and adverse environments in real settings like vehicles and aircraft cockpits.
2) Adverse environments mismatch the training and testing conditions of speech recognition systems. Major causes of adverse environments include noise, distortions from equipment, and changes in speech from stress.
3) Several approaches can help compensate for adverse environments, such as multi-style training with varied speech, signal enhancement preprocessing to remove noise, and developing robust representations of speech that are resistant to noise corruption. Properly applying such techniques can improve the performance of speech recognition in noisy real-world conditions.