交叉混合卷积神经网络用于数字语音识别
Quoc Bao Diep1, Hong Yen Phan1, Thanh-Cong Truong2
1Faculty of Mechanical - Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City, Vietnam.
PloS one
|April 26, 2024
概括
卷积神经网络 (CNN) 为数字语音识别提供了先进的解决方案,优于传统方法. 这些模型有效地学习复杂的音频功能,提高了语音命令等应用程序的准确性和速度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 数字语音识别在捕捉复杂的音频信号特征,如频率,音调和音色方面面临挑战.
- 传统的方法与精确语音识别所需的复杂特征作斗争.
- 卷积神经网络 (CNN) 是解决这些局限性的有希望的方法.
研究的目的:
- 介绍和评估三种基于CNN的新型数字语音识别模型.
- 为了证明CNN在学习语音信号特征方面比现有模型更优越.
- 提高语音识别系统的准确性和效率.
主要方法:
- 三个CNN架构的开发:1D-CNN用于直接的数据学习.
- 实现2D-CNN和2DM-CNN,利用里埃变换进行波形到图像的转换.
- 在四个大型数据集上进行培训和测试,每个数据集包括3万个样本.
主要成果:
- 拟议的CNN模型显著超过了像GoogleLeNet和AlexNet这样的既定模型.
- 实现了高准确率,最好的模型达到95.87%,99.65%和99.76%.
- 在准确性和速度方面,与其他模型相比,表现出5-10%的性能改进.
结论:
- 开发的CNN模型有效地学习复杂的语音特征,从而提高识别能力.
- 与现有的语音识别解决方案相比,这些模型提供了更高的准确性和速度.
- 拟议的方法有可能在虚拟助理,医疗记录和语音命令系统中广泛应用.
相关概念视频
01:10Reconstruction of Signal using Interpolation
194
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
194
01:24Convolution: Math, Graphics, and Discrete Signals
249
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
249
01:17Convolution Properties II
192
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
192
01:20Convolution Properties I
147
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
147
01:26Linear Approximation in Frequency Domain
89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89