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[电气工程与系统科学] 利用Deep的母语语音进行重音识别 暹罗网络 [推广有奖]

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mingdashike22 在职认证  发表于 2022-3-6 09:30:00 来自手机 |只看作者 |坛友微信交流群|倒序 |AI写论文

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摘要翻译:
口音的自动识别问题对于说话人特征分析和识别等应用以及语音识别系统的改进都具有重要意义。语音的重音性主要归因于说话人的母语对给定语音记录的影响。在本文中,我们提出了一个新的口音识别系统,其训练利用母语语音和重音语音。具体来说,我们开发了一个基于深暹罗网络的模型,该模型学习重音语音记录和母语语音记录之间的关联。用无监督高斯混合模型(GMM)或有监督深度神经网络(DNN)模型从语音记录中提取的I向量特征对暹罗网络进行训练。我们使用CSLU外国重音英语(FAE)语料库进行了几个重音识别实验。在这些实验中,我们提出的使用深度暹罗网络的方法在10类重音识别任务上比使用GMM I-向量的基线DNN分类系统获得了15.4%的显著相对性能提高。此外,我们还对所提出的口音识别系统进行了详细的误差分析。
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英文标题:
《Leveraging Native Language Speech for Accent Identification using Deep
  Siamese Networks》
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作者:
Aditya Siddhant, Preethi Jyothi, Sriram Ganapathy
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最新提交年份:
2018
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分类信息:

一级分类:Computer Science        计算机科学
二级分类:Computation and Language        计算与语言
分类描述:Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意,人工语言(编程语言、逻辑学、形式系统)的工作,如果没有明确地解决广义的自然语言问题(自然语言处理、计算语言学、语音、文本检索等),就不适合这个领域。
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一级分类:Computer Science        计算机科学
二级分类:Machine Learning        机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Computer Science        计算机科学
二级分类:Sound        声音
分类描述:Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面,以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5,并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类:Electrical Engineering and Systems Science        电气工程与系统科学
二级分类:Audio and Speech Processing        音频和语音处理
分类描述:Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号,以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括:听觉建模和助听器;声波束形成与声源定位;声场景分类;说话人分离;有源噪声控制和回声消除;增强;去混响;生物声学;音乐信号的分析、合成与修饰;音乐信息检索;多媒体音频和联合音视频处理;口语和书面语建模、切分、标注、句法分析、理解和翻译;文本挖掘;言语产生、感知和心理声学;语音分析、合成、感知建模和编码;鲁棒语音识别;说话人识别与特征描述;应用于语音、音频和语言信号的深度学习、在线学习和图形模型;以及从系统架构到快速算法的实现方面。
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英文摘要:
  The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems. The accented nature of speech can be primarily attributed to the influence of the speaker's native language on the given speech recording. In this paper, we propose a novel accent identification system whose training exploits speech in native languages along with the accented speech. Specifically, we develop a deep Siamese network-based model which learns the association between accented speech recordings and the native language speech recordings. The Siamese networks are trained with i-vector features extracted from the speech recordings using either an unsupervised Gaussian mixture model (GMM) or a supervised deep neural network (DNN) model. We perform several accent identification experiments using the CSLU Foreign Accented English (FAE) corpus. In these experiments, our proposed approach using deep Siamese networks yield significant relative performance improvements of 15.4 percent on a 10-class accent identification task, over a baseline DNN-based classification system that uses GMM i-vectors. Furthermore, we present a detailed error analysis of the proposed accent identification system.
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PDF链接:
https://arxiv.org/pdf/1712.08992
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关键词:deep Dee Applications Architecture Modification GMM system recordings 深度 speech

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