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DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis(cs.S

原文:Automatic speaker recognition algorithms typically use physiological speech characteristics encoded Such algorithms do not capitalize on the complementary and discriminative speaker-dependent characteristics The speaker recognition performance is further improved by combining DeepTalk with a state-of-the-art physiological speech feature-based speaker recognition system. DEEPTALK- VOCAL STYLE ENCODING FOR SPEAKER RECOGNITION AND SPEECH SYNTHESIS.pdf

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2022数据技术嘉年华 Call for Speaker 正式启动!

为此,2022数据技术嘉年华“Call for Speaker”正式启动!我们诚邀您站上舞台、分享您的真知灼见,与参会来宾共话数据技术的未来。   call for  speaker 招募对象 对数据库的行业应用有深入实践或对数据库开发、管理有独特见解和深入思考的数据库/数据技术领域从业者,包括但不局限于行业用户、技术专家、资深DBA/开发者/架构师

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    ·d-vector解读(Deep Neural Networks for Small Footprint Text-Dependent Speaker Verification)

    Deep Neural Networks for Small Footprint Text-Dependent Speaker Verification 目录 ABSTRACT 1. INTRODUCTION 说话者验证(Speaker verification.SV)是基于来自他/她的语音信号的信息接受或拒绝说话者的身份认证的任务。 Dumouchel,“Speaker and session variability in GMM-based speaker verification,” IEEE Transactions on Audio Kenny, “Bayesian speaker verification with heavy-tailed priors,” in Proc. Speaker Recognition, Identification and Verification, 1994

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    从Black Hat Speaker到国内外研究者:强化学习的安全应用

    人工智能技术下的分支有很多,有机器学习、深度学习、强化学习、联邦学习等。笔者直观的理解,强化学习较其他人工智能技术而言,擅长决策和多步决策,大名鼎鼎的Alpha...

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    业界 | 百度提出Deep Speaker:可用于端到端的大规模说话人识别

    我们还发现 Deep Speaker 可以学习到独立于语言的特征。当仅在普通话语音上训练时,Deep Speaker 在英语的验证和识别任务上分别实现了 5.57% 的 EER 和 88% 的准确度。 有关 Deep Speaker 模型、训练技术和实验结果的详情,请参阅论文,以下是该论文的摘要: 论文:Deep Speaker:一种端到端神经说话人嵌入系统(Deep Speaker: an End-to-End Neural Speaker Embedding System) ? 我们提出了 Deep Speaker,这是一个基于神经网络的说话人嵌入系统(neural speaker embedding system),这个系统可以将话语映射到一个超平面,从而可以通过余弦相似度来衡量说话人的相似度 图 1:Deep Speaker 架构示意图 ?

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    情感语言者的身份转换:情感风格与说话人身份的分离研究

    原文题目:IDENTITY CONVERSION FOR EMOTIONAL SPEAKERS: A STUDY FOR DISENTANGLEMENT OF EMOTION STYLE AND SPEAKER identity and speaker-dependent emotion style. an expressive voice conversion framework which can effectively disentangle linguistic content, speaker At run-time, our proposed framework can convert both speaker identity and speaker-dependent emotional IDENTITY CONVERSION FOR EMOTIONAL SPEAKERS___A STUDY FOR DISENTANGLEMENT OF EMOTION STYLE AND SPEAKER

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    分离引导的讲话者日记在现实的不匹配条件

    原文标题:Separation Guided Speaker Diarization in Realistic Mismatched Conditions 原文:We propose a separation guided speaker diarization (SGSD) approach by fully utilizing a complementarity of speech separation and speaker clustering. has the potential to handle the speaker overlap regions. Separation Guided Speaker Diarization in Realistic Mismatched Conditions.pdf

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    使用语音增强和注意力模型的说话人识别系统(CS SD)

    原文题目:Robust Speaker Recognition Using Speech Enhancement And Attention Model 原文:In this paper, a novel architecture for speaker recognition is proposed by cascading speech enhancement and speaker processing Its aim is to improve speaker recognition performance when speech signals are corrupted by noise. Instead of individually processing speech enhancement and speaker recognition, the two modules are integrated To evaluate speaker identification and verification performance of the proposed approach, we test it

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    【NLP】自然语言处理学习笔记(二)语音转换

    另一种解决方式是保持Speaker Encoder不变,在Content Encoder后面加上一个Speaker Classifier作为鉴别器(Discriminator)。 在训练Content Encoder的过程中,同时训练Speaker Classifer,形成对抗结构。如果Content Encoder的效果好,那么Speaker Classifer的效果就越差。 利用Speaker Classifer的效果来反推Content Encoder的效果,因此目标是Speaker Classifer的准确率越低越好。 AdaIN的步骤是先将Decoder出来的结果标准化(IN),然后再将Speaker Encoder的结果用下图中的公式进行添加。 2nd Stage Training是保持训练场景和测试场景一致,即Content Encoder和Speaker Encoder是不同的说话人(下图中Speaker Encoder简化成了独热码)。

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    Windows 10 IoT Serials 9 – 如何利用IoTCoreAudioControlTool改变设备的音频设备

    系统默认是使用了树莓派自带的3.5mm Speaker作为音频输出,使用USB声卡的Microphone作为输入。 下面以Speaker为例,如果要将USB声卡的Speaker设备为默认的Speaker,那么,我们可以先罗列一下音频设备。 设置完成以后,可以查看Windows Device Portal,发现其Audio Device中的Speaker设备已经更改了,如下图所示: ?     0.0.0.00000000}.{4846a864-a89c-435f-9f05-8098bcd7b5d5}     设置完成以后,可以查看Windows Device Portal,发现其Audio Device中的Speaker

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    机器人体验营笔记(三)进阶

    } driver: Forward(-100,10) speaker: Say('beep',duration_scalar=0.8,abort_on_stop =True) =C=> speaker {driver,speaker} =C=> finisher: Say('Safety first!') ) .set_parent(self) driver = Forward(-100,10) .set_name("driver") .set_parent(self) speaker = Say('beep',duration_scalar=0.8,abort_on_stop=True) .set_name("speaker") .set_parent(self) ") completiontrans2 .add_sources(driver,speaker) .add_destinations(finisher)

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    Best Bluemix Content

    required) Building an Android App Using MobileData Cloud Building Highly Scalable Apps for Bluemix, Speaker Bluemix Mobile Quality Assurance: Continuous Quality for Mobile Apps – Reduce Your Time to Feedback, Speaker Speaker: Swaminathan Chandrasekaran Hands On: Building Your Own Watson Powered Application on Bluemix , Speaker: Chris Madison, Watson Solution Architect Using Watson to Build Cognitive Internet of Things iOS, Speakers: Carlos Santana & Belinda Johnson How to Use Microservices to Build a REAL Cloud App, Speaker

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    基于ONOS的SDN-IP架构概述篇

    每个连接点包含以下信息:SDN交换机的DPID、交换机Port和连接的BGP Speaker路由器的MAC地址。 2.3 SDN控制平面连通 在SDN网络和SDN-IP应用程序实例中BGP Speaker通信使用iBGP。对等会话在控制平面中创建,因此,每一个BGP Speaker连接到它。 在任何的BGP部署中 BGP Speaker和SDN-IP应用实例是互连的:一个完整的iBGP网状、路由反射等。 每个SDN-IP实例能够接收来自BGP Speaker的BGP更新,确保多个内部BGP Speaker部署在SDN网络中。 默认情况下,SDN-IP应用将接受所有BGP 开放消息,并会自动配置来使用原始BGP Speaker的AS号。

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    【Rust 日报】2020-04-29 windows GUI 工具包

    样例: let speaker = sonor::find("your room name", Duration::from_secs(2)).await? ("The volume is currently at {}", speaker.volume().await?); match speaker.track().await? ("- No track currently playing"), } speaker.clear_queue().await? ; speaker.join("some other room").await?; https://docs.rs/sonor/0.1.2/sonor/ ---- From 日报小组 挺肥

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    使用 PHP WorkerMan 构建 WebSocket 全双工群聊通信

    = $event_data['speaker']; $class = $event_data['class_id']; $array = Lazer::table(' messages')->limit(1)->where('id', '=', (int) $mes_id)->andWhere('speaker', '=', (int) $speaker)->andWhere $array[0]['speaker']) { global $group_con_map; if (isset($group_con_map[$thread ']; $user_name = $data['speaker_name']; @$mes_id = $data['mes_id']; if (! ":' + antd.user.id + ',"speaker_name":"' + antd.user.info.name + '","mes_id":' + res.data.code + '}')

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    金融语音音频处理学术速递

    Speaker Recognition Challenge 2021 (VoxSRC-21). First, a front-end speaker embedding model is trained to embed utterance and speaker profiles. 摘要:This paper describes the XMUSPEECH speaker recognition and diarisation systems for the VoxCeleb Speaker First, a front-end speaker embedding model is trained to embed utterance and speaker profiles. 摘要:This paper describes the XMUSPEECH speaker recognition and diarisation systems for the VoxCeleb Speaker

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    Wavesplit:通过说话者聚类实现端到端的语音分离(CS SD)

    原文题目:Wavesplit: End-to-End Speech Separation by Speaker Clustering 原文:We introduce Wavesplit, an end-to-end From a single recording of mixed speech, the model infers and clusters representations of each speaker Our model infers a set of speaker representations through clustering, which addresses the fundamental Moreover, the sequence-wide speaker representations provide a more robust separation of long, challenging

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    半自动人工智能回复了解一下

    这里需要进入到文件传输助手中查看 itchat.run() 然后再结合语音助手 语音助手之前也不再重述,就是 pip install Pywin32 然后直接看代码 import win32com.client speaker = win32com.client.Dispatch("SAPI.SpVoice") speaker.Speak("开始说话") O ^ ~ ^ O 然后我们将这些代码组合起来,看一下会有什么效果! ['Text'] #接收文本消息 toName = msg['ToUserName'] #接收方 if toName == "filehelper": speaker = win32com.client.Dispatch("SAPI.SpVoice")#调用语音助手 speaker.Speak(message) if __name__=="__main

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    金融语音音频处理学术速递

    摘要:In the existing cross-speaker style transfer task, a source speaker with multi-style recordings is necessary to provide the style for a target speaker. the timber of another speaker bypassing the dependency on a single speaker's multi-style corpus. 摘要:In the existing cross-speaker style transfer task, a source speaker with multi-style recordings is the timber of another speaker bypassing the dependency on a single speaker's multi-style corpus.

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    共享任务:稳健的口语识别

    languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have different domains than desired application scenarios, demanding a need for domain and speaker-invariant identification sought to investigate just this scenario: systems were to be trained on largely single-speaker We see that domain and speaker mismatch proves very challenging for current methods which can perform

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