Automatic Speech Recognition (ASR) systems are used in the financial domain
to enhance the caller experience by enabling natural language understanding and
facilitating efficient and intuitive interactions. Increasing use of ASR
systems requires that such systems exhibit very low error rates. The
predominant ASR models to collect numeric data are large, general-purpose
commercial models -- Google Speech-to-text (STT), or Amazon Transcribe -- or
open source (OpenAI's Whisper). Such ASR models are trained on hundreds of
thousands of hours of audio data and require considerable resources to run.
Despite recent progress large speech recognition models, we highlight the
potential of smaller, specialized "micro" models. Such light models can be
trained perform well on number recognition specific tasks, competing with
general models like Whisper or Google STT while using less than 80 minutes of
training time and occupying at least an order of less memory resources. Also,
unlike larger speech recognition models, micro-models are trained on carefully
selected and curated datasets, which makes them highly accurate, agile, and
easy to retrain, while using low compute resources. We present our work on
creating micro models for multi-digit number recognition that handle diverse
speaking styles reflecting real-world pronunciation patterns. Our work
contributes to domain-specific ASR models, improving digit recognition
accuracy, and privacy of data. An added advantage, their low resource
consumption allows them to be hosted on-premise, keeping private data local
instead uploading to an external cloud. Our results indicate that our
micro-model makes less errors than the best-of-breed commercial or open-source
ASRs in recognizing digits (1.8% error rate of our best micro-model versus 5.8%
error rate of Whisper), and has a low memory footprint (0.66 GB VRAM for our
model versus 11 GB VRAM for Whisper).

我们的研究致力于创建数字识别的微型模型，以处理反映现实世界发音模式的不同讲话样式，并且相较于商业或开源的 ASR 系统，我们的微型模型在识别数字方面减少了错误率（我们最佳微型模型的错误率为 1.8%，而 Whisper 错误率为 5.8%），而且占用的内存空间较低（我们的模型为 0.66 GB VRAM，而 Whisper 为 11 GB VRAM）。

精确安全交易的数字微模型

Digits micro-model for accurate and secure transactions

Recognizing handwritten digits is a challenging task primarily due to the
diversity of writing styles and the presence of noisy images. The widely used
MNIST dataset, which is commonly employed as a benchmark for this task,
includes distorted digits with irregular shapes, incomplete strokes, and
varying skew in both the training and testing datasets. Consequently, these
factors contribute to reduced accuracy in digit recognition. To overcome this
challenge, we propose a two-stage deep learning approach. In the first stage,
we create a simple neural network to identify distorted digits within the
training set. This model serves to detect and filter out such distorted and
ambiguous images. In the second stage, we exclude these identified images from
the training dataset and proceed to retrain the model using the filtered
dataset. This process aims to improve the classification accuracy and
confidence levels while mitigating issues of underfitting and overfitting. Our
experimental results demonstrate the effectiveness of the proposed approach,
achieving an accuracy rate of over 99.5% on the testing dataset. This
significant improvement showcases the potential of our method in enhancing
digit classification accuracy. In our future work, we intend to explore the
scalability of this approach and investigate techniques to further enhance
accuracy by reducing the size of the training data.

我们提出了一种两阶段深度学习方法，用于识别手写数字，通过过滤失真和模糊图像来改善分类准确度，并在测试数据集上达到 99.5% 的准确率。

MNIST 手写数字图像的修剪

Pruning Distorted Images in MNIST Handwritten Digits

Neural networks have achieved impressive breakthroughs in both industry and
academia. How to effectively develop neural networks on quantum computing
devices is a challenging open problem. Here, we propose a new quantum neural
network model for quantum neural computing using (classically-controlled)
single-qubit operations and measurements on real-world quantum systems with
naturally occurring environment-induced decoherence, which greatly reduces the
difficulties of physical implementations. Our model circumvents the problem
that the state-space size grows exponentially with the number of neurons,
thereby greatly reducing memory requirements and allowing for fast optimization
with traditional optimization algorithms. We benchmark our model for
handwritten digit recognition and other nonlinear classification tasks. The
results show that our model has an amazing nonlinear classification ability and
robustness to noise. Furthermore, our model allows quantum computing to be
applied in a wider context and inspires the earlier development of a quantum
neural computer than standard quantum computers.

本研究提出了一种新的量子神经网络模型，使用实际存在的环境诱导的退相干的量子系统上的 (经典控制的) 单量子比特操作和测量，从而极大地降低了物理实现的难度。该模型在手写数字识别和其他非线性分类任务中具有优异的非线性分类能力和对噪声的鲁棒性。

量子神经网络用于量子神经计算

Quantum Neural Network for Quantum Neural Computing

We describe and analyze efficient algorithms for learning a linear predictor
from examples when the learner can only view a few attributes of each training
example. This is the case, for instance, in medical research, where each
patient participating in the experiment is only willing to go through a small
number of tests. Our analysis bounds the number of additional examples
sufficient to compensate for the lack of full information on each training
example. We demonstrate the efficiency of our algorithms by showing that when
running on digit recognition data, they obtain a high prediction accuracy even
when the learner gets to see only four pixels of each image.

本文提出了一种高效的线性预测算法，用于从样本中仅看到少数属性的情况下学习，通过医学研究和数字识别数据的实验证明其精度高。