基于最小统计噪声估计的信号子空间语音增强

针对传统子空间方法中,采用语音活动检测（Voice activity detection,VAD）估计噪声的缺陷,提出了一种基于子空间域的最小统计噪声估计算法。噪声估计通过跟踪带噪语音协方差矩阵用每个特征向量上的特征值的最小值来获得,该方法不需要VAD明确区分语音段和噪声段,能够在整个信号期间实现噪声的连续估计和不断更新。实验结果表明,相对于传统的基于VAD的子空间方法,本文提出的算法对语音增强效果有非常显著的提高。     

噪音环境下的语音识别研究

文章详细介绍了一些常用的去噪处理方法,也介绍了笔者在抗噪语音识别方面的研究工作,文章最后给出了很有潜力的一些抗噪识别方式。     

Rayleigh Mixture Model-Based Hidden Markov Modeling and Estimation of Noise in Noisy Speech Signals

In this paper, we propose a new statistical model for noise periodogram modeling and estimation. The proposed model is a hidden Markov model (HMM) with a Rayleigh mixture model (RMM) in each state. For this new model, we derive an expectation-maximization (EM) training algorithm and a minimum mean-square error (MMSE) noise periodogram estimator. It is shown that when compared to the Gaussian mixture model (GMM)-based HMM, the RMM-based HMM has less computationally complex EM iterations and gives a better fit of the noise periodograms when the mixture models has a low number of components. Furthermore, we propose a specialization of the proposed model, which is shown to provide better MMSE noise periodogram estimates than any other of the tested HMM initializations for cyclo-stationary noise types     

有色噪声环境中鲁棒语音特征参数提取研究

针对复杂噪声干扰环境中语音特征参数会发生改变,引起训练模型和测试语音之间的失配,使语音识别系统的识别率降低,为提高语音特征参数在色噪声环境中提取的鲁棒性,提出了基于总体最小二乘旋转不变子空间技术(TLS-ESPRIT)谐波倒谱加权谱鲁棒特征参数提取方法.运用TLS-SVD方法对观测数据矩阵进行广义特征值分解估计谐波模型的参数,实现了有色噪声背景下语音信号的最优估计.在重建语音的过程中根据谐波能量与带噪语音能量的比值,对重建谐波的各个谐波峰给予不同的加权和语音建模,并进行仿真,结果实现了鲁棒性特征参数的提取,解决了模型之间的失配问题.     

噪音环境下孤立词的语音识别

噪音环境下的语音识别一直是语音识别的难点，本文采用了谱减法进行去噪，进行孤立词（数字0-9）的识别，提高系统的识别率     

噪音环境下孤立词的语音识别

噪音环境下的语音识别一直是语音识别的难点,本文采用了谱减法进行去噪,进行孤立词(数字0-9)的识别,提高系统的识别率.     

飞机驾驶舱噪声环境下的飞行员语音端点检测

为在飞行驾驶舱噪声环境下准确判定飞行员语音端点,提出一种鲁棒语音端点检测方法。使用最优改进对数谱幅度估计语音增强算法进行初步语音降噪,通过Teager能量算子进一步滤除残余噪声,并将降噪后语音短时能量与子带谱熵的比值作为双门限判决参数,检测飞行员语音起止点。实验结果表明,与基于能量参数或频谱熵参数的语音端点检测方法相比,该方法能有效提高检测正确率。     

基于间接拉普拉斯模型因子估计的语音增强算法

对DCT城基于拉普拉斯统计模型的语音增强,分析了模型因子的估计误差及其对于算法整体增强性能的影响,并根据广义高斯分布模型度其形态参数的概念与性质,提出了一种新的拉普拉斯模型因子估计方法,该方法结构简单,它利用拉普拉斯模型条件下语音分量方差与模型因子的对应关系,间接地获取模型因子的估计,算法不仅有效地消除了噪声分量对于估计精度的影响,而且可以快速地跟踪语音分量的变化。仿真结果表明,基于该模型因子估计方法的语音增强算法在多种噪声背景下具有更出色的语音增强效果。     

复杂噪声环境下语音识别研究

语音识别是人机交互的重要方式,针对传统语音识别系统对含噪语音识别性能较差、特征选择不恰当的问题,提出一种基于迁移学习的深度自编码器循环神经网络模型.该模型由编码器、解码器以及声学模型组成,其中,声学模型由堆栈双向循环神经网络构成,用于提升识别性能;编码器和解码器均由全连接层构成,用于特征提取.将编码器结构及参数迁移至声...     

Adaptive Regulator for Hammerstein and Wiener Systems With Noisy Observations

For Hammerstein and Wiener systems observed with additive noises the adaptive regulation control is produced by a truncated stochastic approximation (SA) algorithm with truncation regions expanding with a prescribed rate. It is proved that the stochastic adaptive control given in this note is optimal in the sense that it minimizes the long run average of regulation errors a.s     

Online Noise Estimation Using Stochastic-Gain HMM for Speech Enhancement

We propose a noise estimation algorithm for single-channel noise suppression in dynamic noisy environments. A stochastic-gain hidden Markov model (SG-HMM) is used to model the statistics of nonstationary noise with time-varying energy. The noise model is adaptive and the model parameters are estimated online from noisy observations using a recursive estimation algorithm. The parameter estimation is derived for the maximum-likelihood criterion and the algorithm is based on the recursive expectation maximization (EM) framework. The proposed method facilitates continuous adaptation to changes of both noise spectral shapes and noise energy levels, e.g., due to movement of the noise source. Using the estimated noise model, we also develop an estimator of the noise power spectral density (PSD) based on recursive averaging of estimated noise sample spectra. We demonstrate that the proposed scheme achieves more accurate estimates of the noise model and noise PSD, and as part of a speech enhancement system facilitates a lower level of residual noise.     

Feature Enhancement for Noisy Speech Recognition With a Time-Variant Linear Predictive HMM Structure

This paper presents a new approach for speech feature enhancement in the log-spectral domain for noisy speech recognition. A switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution. Each multivariate linear dynamic model (LDM) is associated with the hidden state of a hidden Markov model (HMM) as an attempt to describe the temporal correlations among adjacent frames of speech features. The state transition on the Markov chain is the process of activating a different LDM or activating some of them simultaneously by different probabilities generated by the HMM. Rather than holding a transition probability for the whole process, a connectionist model is employed to learn the time variant transition probabilities. With the resulting SLDM as the speech model and with a model for the noise, speech and noise are jointly tracked by means of switching Kalman filtering. Comprehensive experiments are carried out using the Aurora2 database to evaluate the new algorithm. The results show that the new SLDM approach can further improve the speech feature enhancement performance in terms of noise-robust recognition accuracy, since the transition probabilities among the LDMs can be described more precisely at each time point.     

多模噪声环境下的自适应滤波算法

在分析现有基于不同误差准则的最小四次均方算法和最小平均绝对偏差算法的基础上,提出一种可抑制多模噪声的归一化混合范数滤波算法。采用引入混合参数的代价函数调节混合参数得到最佳的滤波算法。建立步长参数随输入信噪比变化的非线性函数关系。仿真结果表明,相对于高斯环境下的滤波,该算法在多模噪声环境下的鲁棒性得到显著提高,能有效地提取信号。     

噪声环境下语音信号端点检测算法的研究与改进

端点检测是语音识别申的一项关键技术，端点检测的准确性对语音识别的性能有很大影响。论文对基于短时能量和短时过零率及基于LPC倒谱特征的端点检测算法进行了研究，给出改进的基于LPC美尔倒谱特征的端点检测算法，并通过实验证明其在低信噪比下具有较好的检测性能。随着语音识别技术的发展，这种算法在实际应用中的高效率、实时、准确性会逐渐显现出。     

强脉冲噪声环境下的多径衰信道估计

提出一种在强干扰脉冲噪声存在下对无线多径信道进行估计的算法.在无线通信系统中,衰落信道可以采用自回归(AR)模型建模,通过RLS算法和自适应Kalman滤波器分别对AR模型的参数进行估计,但是,这两种算法对噪声干扰非常敏感.为了加快RLS算法的收敛性,并有效抑制大脉冲干扰的影响,在算法的改进中引入了抑制因子,用于对脉冲干扰幅度的抑制.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估参数的收敛速度.     

卡尔曼滤波系统和量测噪声自适应估计的关联性

卡尔曼滤波是惯导系统(INS)/GPS组合导航的主要算法之一,Sage-Husa算法是在卡尔曼滤波基础上,为减少系统噪声和量测噪声的不确定性对误差估计的影响而采用的自适应估计方法.对Sage-Husa算法提出了4条改进措施;并通过在3种数据扰动情形下的仿真计算发现,只对一类噪声做自适应估计更容易产生较大的偏差,对系统噪声和量测噪声两类噪声同时做自适应估计,其效果要优于只对一类噪声做自适应估计,把此现象定义为卡尔曼滤波的系统和量测噪声自适应估计的关联性.这个结果不同于一些文献的观点.此项研究对自适应卡尔曼滤波在INS/GPS组合导航的工程化应用有较高的实用价值.     

Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition

In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.      

一种高噪声环境下语音检测算法的仿真与应用

该文提出了一种高噪声环境下的自适应语音检测新算法。该算法利用了语音短时能量及帧内短时自相关的特性,两个自适应判决门限根据期望的误判率调整,无需事先给出噪声统计信息,适用于平稳噪声、缓变的非平稳噪声及脉冲噪声的情况,且可以实时实现。