CNNs often replace previously popular methods like HMMs and GMM-UBM in various cases.
Moreover, CNNs possess the ability to acquire features that remain robust despite variations in
speech signals resulting from diverse speakers, accents, and background noise. This is made possible
due to three key properties of CNNs: locality, weight sharing, and pooling. The locality property
enhances resilience against non-white noise by enabling the computation of effective features from
cleaner portions of the spectrum. Consequently, only a smaller subset of features is affected by
the noise, allowing higher network layers a better opportunity to handle the noise by combining
higher-level features computed for each frequency band. This improvement over standard fully
connected neural networks, which process all input features in the lower layers, highlights the
significance of locality. As a result, locality reduces the number of network weights that must be
learned.
AReviewofDeepLearningTechniquesforSpeechProcessing