Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Speaker identification investigation and analysis in unbiased and biased emotional talking environments

This work aims at investigating and analyzing speaker identification in each unbiased and biased emotional talking environments based on a classifier called Suprasegmental Hidden Markov Models (SPHMMs). The first talking environment is unbiased towards any emotion, while the second talking environment is biased towards different emotions. Each of these talking environments is made up of six distinct emotions. These emotions are neutral, angry, sad, happy, disgust and fear. The investigation and analysis of this work show that speaker identification performance in the biased talking environment is superior to that in the unbiased talking environment. The obtained results in this work are close to those achieved in subjective assessment by human judges.     

Identifying speakers using their emotion cues

This paper addresses the formulation of a new speaker identification approach which employs knowledge of emotional content of speaker information. Our proposed approach in this work is based on a two-stage recognizer that combines and integrates both emotion recognizer and speaker recognizer into one recognizer. The proposed approach employs both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. In the experiments, six emotions are considered including neutral, angry, sad, happy, disgust and fear. Our results show that average speaker identification performance based on the proposed two-stage recognizer is 79.92% with a significant improvement over a one-stage recognizer with an identification performance of 71.58%. The results obtained based on the proposed approach are close to those achieved in subjective evaluation by human listeners.     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

Symbolic Place Recognition in Voronoi-Based Maps by Using Hidden Markov Models

This article presents a new algorithm to recognize natural distinctive places such as corridors, halls, narrowings, corridors with doors opening on the left side, etc., from indoor environments using Hidden Markov Models (HMM). HMM give a stochastic solution which can be used to make decisions on localization, navigation and path-planning. The environment is modeled as a topo-geometric map which combines topological and geometric information. This map is obtained from a Voronoi diagram using measurements of a laser telemeter. The characteristics of topo-geometric map (nodes, number of edges adjacent to nodes, slope of edges, etc.) are used to learn and to recognize the different places typical of indoor environments. This map can be used in order to resolve several problems in robotics such as localization, navigation and path-planning. Our method of place recognition is a fast and effective way for a robot to recognize typical places of indoor environments from a topo-geometric map.     

WSNs中基于隐马尔科夫模型的目标识别问题研究

由于无线传感器网络（Wireless Sensor Networks,WSNs）资源受限,如何有效利用资源,提高目标辨别的准确度,是WSNs中目标识别系统的研究难题。以隐马尔科夫模型为分类框架,对一个声音传感器阵列节点簇内的目标识别问题进行建模;基于节点信号的空间关联性,改进了子节点Viterbi最大似然序列的计算状态,设置了子节点报送间隔,从而有效地判别局部状态。实验证明,改进后的算法在维持判别正确率的同时降低信息传输量10%以上。     

Neural network based feature transformation for emotion independent speaker identification

In this paper we are proposing neural network based feature transformation framework for developing emotion independent speaker identification system. Most of the present speaker recognition systems may not perform well during emotional environments. In real life, humans extensively express emotions during conversations for effectively conveying the messages. Therefore, in this work we propose the speaker recognition system, robust to variations in emotional moods of speakers. Neural network models are explored to transform the speaker specific spectral features from any specific emotion to neutral. In this work, we have considered eight emotions namely, Anger, Sad, Disgust, Fear, Happy, Neutral, Sarcastic and Surprise. The emotional databases developed in Hindi, Telugu and German are used in this work for analyzing the effect of proposed feature transformation on the performance of speaker identification system. In this work, spectral features are represented by mel-frequency cepstral coefficients, and speaker models are developed using Gaussian mixture models. Performance of the speaker identification system is analyzed with various feature mapping techniques. Results have demonstrated that the proposed neural network based feature transformation has improved the speaker identification performance by 20?%. Feature transformation at the syllable level has shown the better performance, compared to sentence level.     

结合HMM和SPSM的手语识别方法

手语识别的研究具有重大的学术价值和广泛的应用前景。在近些年的手语识别工作中,隐马尔可夫模型(Hidden Markov Models,简称HMMs)起到了重要的作用,但是,HMMs假设同一状态内的观察值之间是独立同分布的,这个假设同某些手语信号的帧间相关性相背离。受到多项式片段模型(Polynomial Segment Models,简称PSMs)能够显式描述帧间相关性的启发,提出了一种简化的PSMs,其中应用马氏距离作为距离测度。实验表明,这种简化的PSMs在同传统的HMMs进行后验概率归一化求和的融合之后,手语词的平均相对正确率得到了13.38%的提升,从而证明此方法是一种更加精确的手语识别方法。     

A Novel framework of EEG-based user identification by analyzing music-listening behavior

This paper introduces a novel framework for user identification by analyzing neuro-signals. Studies regarding Electroencephalography (EEG) revealed that such bio-signals are sensitive, hard to forge, confidential, and unique which the conventional biometric systems like face, speaker, signature and voice lack. Traditionally, researchers investigated the neuro-signal patterns by asking users to perform various imaginary, visual or calculative tasks. In this work, we have analyzed this neuro-signal pattern using audio as stimuli. The EEG signals are recorded simultaneously while user is listening to music. Four different genres of music are considered as users have their own preference and accordingly they respond with different emotions and interests. The users are also asked to provide music preference which acts as a personal identification mechanism. The framework offers the benefit of uniqueness in neuro-signal pattern even with the same music preference by different users. We used two different classifiers i.e. Hidden Markov Model (HMM) based temporal classifier and Support Vector Machine (SVM) for user identification system. A dataset of 2400 EEG signals while listening to music was collected from 60 users. User identification performance of 97.50 % and 93.83 % have been recorded with HMM and SVM classifiers, respectively. Finally, the performance of the system is also evaluated on various emotional states after showing different emotional videos to users.     

隐式半马尔科夫模型下的变压器故障诊断方法

变压器运行过程中存在多种状态,能够正确划分运行状态,对变压器的维修和故障诊断有着重要的意义。首先,详细分析了马尔科夫链的衍生模型,并构造了隐式半马尔科夫模型(Hidden Semi-Markov Models, HSMM);然后,通过引入“微状态-宏状态”的对应关系,用于在HSMM中描述变压器运行过程中的状态;最后,建立了涵盖变压器历史状态信息,并包含特征提取、状态分类和故障识别过程的HSMM故障诊断流程。通过变压器DGA故障诊断的算例分析,结果表明所述方法的有效性。     

说话人识别及其应用的研究

虽然理论上隐马尔可夫模型（HMM）是较为有效的一种说话人识别方法，但传统的模型训练方法──Baum－Welch算法不仅运算量和存储量较大，而且若因经验不足、模型初值设置不当会导致算法发散或迭代收敛到非全局最优点。本文提出一种新的方法，将状态分割、动态聚类、模糊统计与传统的Baum－Welch算法相结合应用于说话人识别，既降低了运算量和存储量，又避免了因初值设置不当而导致算法迭代收敛到非全局最优点。本文在大量实验的基础上，建立了说话人识别系统并进行了实验研究，收到了良好的效果。该系统模型数目少，运算复杂度低，可扩充性强，易于训练，便于识别，具有广阔的应用前景。     

手语识别中基于HMM的区分性训练方法

传统的隐马尔科夫模型（HMM）的训练方法基于统计概率的最大似然准则（MLE），在训练样本数目足够大的情况下，这种方法在理论上可以得到最优的结果．在手语识别研究中，采集足够大的训练样本十分困难．区分性训练可以很好地弥补由于训练样本的缺乏以及手语模型之间的近似而造成的识别系统的缺陷．最大交互信息准则（MMIE）作为区分性训练准则的一种已经被广泛的应用于语音识别领域．文中通过合理的构建手语识别中的竞争模型和易混集。提出了MMIE准则的改进形式，并将其应用于特定人与非特定人手语识别．实验证明，使用改进的MMIE准则对识别系统性能有很大的提高．     

A Hybrid Framework for Mobile Robot Localization: Formulation Using Switching State-Space Models

In this paper we address one of the most important issues for autonomous mobile robots, namely their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot's state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model families currently used for this purpose: the Kalman filter Linear models and the Hidden Markov Models. The proposed model combines the advantages of both models, relaxing at the same time inherent assumptions made individually in each of these existing models.     

基于混合因子分析的隐马尔可夫模型

经典隐马尔可夫模型用于语音识别存在的两个主要缺陷是“离散状态假设”和“独立分布假设”。前者忽略了语音信号的非平稳性,后者忽略了语音信号的相关性。文章将混合因子分析方法用于语音建模,提出了基于混合因子分析的隐马尔可夫模型框架,并用动态贝叶斯网络形象地表示。该模型框架不仅从理论上解决了上述问题,而且给出许多语音建模的选择。目前广泛使用的统计声学模型均可视为该模型的特例。     

隐马尔可夫模型的多序列比对研究

研究一种关于隐马尔可夫模型的多序列比对,利用值和特征序列的保守性,通过增加频率因子,改进传统隐马尔可夫模型算法的不足。实验表明,新算法不但提高了模型的稳定性,而且应用于蛋白质家族识别,平均识别率比传统隐马尔可夫算法提高了3.3个百分点。     

基于HMM的联机汉字识别系统及其改进的训练方法

本文描述了一个基于HMM模型的联机汉字识别系统的设计思想与实现方法。系统以联机汉字的笔段序列作为观察序列,采用带有多跨越的模型结构消除自由书写汉字笔段序列的冗余与丢失问题。HMM模型的训练是本系统设计的一个重要问题,针对复杂HMM模型参数训练容易收敛于局部最小的情况,本文结合联机汉字识别的特点,提出了一种利用“引导模型”进行训练的改进方法,避免了训练过程收敛于局部最小点的发生。经过大量样本的训练,本系统对规范书写汉字和自由书写汉字均取得了比较令人满意的结果。     

基于隐马尔可夫模型的非侵入式负荷监测泛化性能改进

隐马尔可夫模型(HMM)是非侵入式负荷监测常用的算法.由于电压波动与负荷自身电气特性变化等原因,负荷的测量状态如功率可能持续变化,运行过程中出现新的状态转移,但当前基于HMM的非侵入式负荷监测方法并未考虑如何处理该情况,缺乏状态辨识与功率分解的泛化能力.针对这一问题,本文提出并构建二元参数隐马尔科夫模型(BPHMM),结合DBSCAN聚类算法,基于有功功率和稳态电流对负荷状态进行聚类,降低了因电压波动和噪声数据对负荷状态聚类结果造成干扰的可能性;改进维特比算法使其考虑到HMM模型参数更新以实现对负荷状态预测泛化性能的改进;考虑到功率的随机波动性,基于极大似然估计原理构建功率计算优化模型并实现负荷的功率分解.本文采用公共数据集AMPds2对所述方法进行验证,测试算例验证了所述方法的有效性.