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Spoken Command Recognition with Quantum Convolutional Neural Networks

Juha Korvenaho

Published Dec 29, 2022• 0 citations
OpenAlex

Abstract

Speech recognition is a hard problem that aims to use machines to understand human speech.This work focuses on the smaller subsection of the speech recognition called, spoken command recognition (SCR).SCR aims to recognize one-word commands instead of full sentences.The area of SCR is well researched and there have been breakthroughs especially when convolutional neural networks (CNN) were introduced.Nowadays SCR is widely used, for example Apple Siri and Amazon Alexa.Recent breakthroughs on the field of quantum computing have made it possible to experiment SCR task with quantum convolutional neural networks (QCNN) and to test if QCNN provides some benefit over classic CNN.This work focuses more on the study by Yang et al. (2021).Yang et al. (2021) proposed a QCNN method for SCR and reported over 95 % accuracy on 10 command dataset.This work extends their study to 35 commands and tests whether or not QCNN performs better with limited amount of training-data or network parameters.According to our experiments, adding more commands reduces model accuracy around 3 %, but QCNN seems to perform better with limited training-data and slightly better with limited parameters.In comparison between CNN and QCNN, CNN seems to perform slightly better than QCNN.

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Speech recognition is a hard problem that aims to use machines to understand human speech.This work focuses on the smaller subsection of the speech recognition called, spoken command recognition (SCR).SCR aims to recognize one-word commands instead of full sentences.The area of SCR is well researched and there have been breakthroughs especially when convolutional neural networks (CNN) were introduced.Nowadays SCR is widely used, for example Apple Siri and Amazon Alexa.Recent breakthroughs on the field of quantum computing have made it possible to experiment SCR task with quantum convolutional neural networks (QCNN) and to test if QCNN provides some benefit over classic CNN.This work focuses more on the study by Yang et al. (2021).Yang et al. (2021) proposed a QCNN method for SCR and reported over 95 % accuracy on 10 command dataset.This work extends their study to 35 commands and tests whether or not QCNN performs better with limited amount of training-data or network parameters.According to our experiments, adding more commands reduces model accuracy around 3 %, but QCNN seems to perform better with limited training-data and slightly better with limited parameters.In comparison between CNN and QCNN, CNN seems to perform slightly better than QCNN.
This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, we put forth a hybrid transfer learning algorithm that allows a pre-trained classical network to be transferred to the classical part of the hybrid QNN model. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.
This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, we put forth a hybrid transfer learning algorithm that allows a pre-trained classical network to be transferred to the classical part of the hybrid QNN model. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.
Quantum machine learning (QML) has emerged as an exciting new technology that explores challenging machine learning issues by relying on advances in quantum computing. A convolutional neural network is primarily used in the classical portion of the QNN-based Speech Recognition System, which is made up of both classical and quantum components. The quantum component is based on the equation’s quantum circuits with a certain learnable parameter. Speech recognition technology is one of the quickly evolving technologies, along with Siri on the iPhone, chatbots, Alexa on the Amazon Echo, etc… The research uses a quantum convolutional neural network (QCNN) to identify voice and convert it to text. The use of Quantum filters will reduce the input in to qubits and then it will be further moving in to the feature extraction and classification process. QCNN gives more accurate results and will increase the scalability by adding more layers in to the model.
The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.
Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications.