Despite the remarkable progress made in the field of SLU, accurately comprehending human
speech in real-life situations continues to pose significant challenges. These challenges are amplified
by the presence of diverse accents, dialects, and linguistic variations. In a notable study, Vanzo et al.
[551] emphasize the significance of SLU in facilitating effective human-robot interaction, particularly
within the context of house service robots. The authors delve into the specific obstacles encountered
in this domain, which encompass handling noisy and unstructured speech, accommodating various
accents and speech variations, and deciphering complex commands involving multiple actions.
To overcome these obstacles, ongoing research endeavors are dedicated to developing innovative
solutions that enhance the precision and efficacy of SLU systems. By addressing these challenges,
the aim is to enable more robust and accurate speech comprehension in diverse real-life scenarios.
AReviewofDeepLearningTechniquesforSpeechProcessing