Quantum Kernel-aided Support Vector Machine Classifier for Improved Speech Classification

The field of machine learning has been revolutionized by quantum computing, as it has the capacity to tackle complex problems that are beyond the capabilities of classical computers. One such problem is the efficient classification of speech signals with limited data, employing extracted feature attributes like Mel-scale frequency cepstral coefficients (MFCCs). This study compares the performance of classical machine learning models with a Quantum Support Vector Machine (QKSVM) classifier on a data set with a limited number of audio samples. Interestingly, the quantum kernel method outperformed classical models despite the small sample size. This result can be attributed to the quantum kernel method’s ability to extract high-dimensional features efficiently, allowing it to learn more from fewer samples. The Quantum Kernel Circuit (QKC) is contrived in different variants comprising 13, 8, 7, and 4 qubits. This QKC is built with an Angle Embedding layer, an adjoint version of the Angle Embedding layer, and a Hermitian space observable layer. Our findings demonstrate that the hybrid quantum kernel-based technique outperforms existing classical solutions. Amongst variants, the Quantum support vector machine model with 13 optimal numbers of coefficients is efficient in speech classification with the highest accuracy of 87.32%.