A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

QKL [15, 12] instead demonstrated a competitive performance on many speech processing provides an alternative training mechanism to use quantum tasks. Nonetheless, training a large parameterized states for quantum encoding and projection [16], and then DNN using as few as 1,000 utterances usually leads to a poor forms a kernel to estimate a hyperplane to separate training speech recognition accuracy. Considering that there exist over data with its kernel alignment. We will discuss these quantum 8, 000 spoken languages [3] in the world, it is clear that some operations in detail later. Recent theoretical studies [12, 17] of those spoken languages may not provide enough training have also proven that QKL requires less trainable parameters materials to properly deploy DNN-based spoken command to archive the same performance of QNNs.