智能语音交互声学的相关内容

3月16日云栖精选夜读：显著超越流行长短时记忆网络，阿里提出DFSMN语音识别声学模型

在语音顶会ICASSP，阿里巴巴语音交互智能团队的poster论文提出一种改进的前馈序列记忆神经网络结构，称之为深层前馈序列记忆神经网络（DFSMN）。研究人员进一步将深层前馈序列记忆神经网络和低帧率（LFR）技术相结合，构建LFR-DFSMN语音识别声学模型。热点热议显著超越流行长短时记忆网络...

显著超越流行长短时记忆网络，阿里提出DFSMN语音识别声学模型

在语音顶会ICASSP，阿里巴巴语音交互智能团队的poster论文提出一种改进的前馈序列记忆神经网络结构，称之为深层前馈序列记忆神经网络（DFSMN）。研究人员进一步将深层前馈序列记忆神经网络和低帧率（LFR）技术相结合，构建LFR-DFSMN语音识别声学模型。该模型在大词汇量的英文识别和中文识别...

达摩院智能语音交互 - 人机对话技术浅析

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阿里巴巴智能语音交互技术与应用

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INTERSPEECH 2017系列 | 语音识别技术之声学模型

编者：今年的INTERSPEECH于8月20日至24日在瑞典的斯德哥尔摩顺利召开，众多的高校研究机构和著名的公司纷纷在本次会议上介绍了各自最新的技术、系统和相关产品，而阿里巴巴集团作为钻石赞助商也派出了强大的阵容前往现场。从10月25日开始，阿里iDST语音团队和云栖社区将共同打造一系列语音技术分享...

语音识别中声学模型得分计算优化方法

摘要语音是人们沟通交流最直接、最自然的交互方式。自计算机问世以来，人们就一直希望可以通过语音实现人和计算机之间的交互，而语音识别技术，目标就是通过把人类的语音中的词汇内容转换为相应的文本，架起了人机交...

人机交互新进展：LFR-DFSMN语音识别声学模型介绍

语音识别技术是人机交互技术的重要组成部分，而语音识别中的声学模型是语音识别技术中的核心所在，堪称重中之重。阿里巴巴iDST智能语音交互团队最新的LFR-DFSMN模型相对于之前的LFR-LCBLSTM模型可以达到训练加速3倍、识别加速2倍、识别错误率降低20%和最终模型大小压缩50%的效果，实现了语...

深度学习在语音识别中的声学模型以及语言模型的应用

过去 3 年，深度学习在各个领域取得较大突破，比如计算机视觉领域的物体识别、场景分类，语音分析等，并且其技术推广和应用的速度超过人们预期,比如 Google 的广告系统已经开始使用深度学习盈利，Twitter 也通过深度学习改善Ap...

95188：BLSTM-DNN hybrid语音识别声学模型的第一个工业应用

双十一当天，蚂蚁金服客户中心整体服务量超过500万人次，94%以上都是通过人工智能技术驱动的自助服务解决。在整个自助服务中，非常重要的一环是呼叫中心的语音转文本服务，这是一个典型的电话语音识别问题。电话语音识别是当今语音识别领域最复杂最困难的问题之一。对话过程中说话人风格自然随意、口音、不流利（重...

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