公开(公告)号：WO2019199554A1

公开(公告)日：2019-10-17

申请号：PCT/US2019/025686

申请日：2019-04-04

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： CHEN, Zhuo ,  ERDOGAN, Hakan ,  YOSHIOKA, Takuya ,  ALLEVA, Fileno A. ,  XIAO, Xiong

IPC: G10L15/16

Abstract: This document relates to separation of audio signals into speaker-specific signals. One example obtains features reflecting mixed speech signals captured by multiple microphones. The features can be input a neural network and masks can be obtained from the neural network. The masks can be applied one or more of the mixed speech signals captured by one or more of the microphones to obtain two or more separate speaker-specific speech signals, which can then be output.

公开(公告)号：WO2015191968A1

公开(公告)日：2015-12-17

申请号：PCT/US2015/035504

申请日：2015-06-12

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： ZHAO, Pei ,  LEUNG, Max ,  YAO, Kaisheng ,  YAN, Bo ,  ZHAO, Sheng ,  ALLEVA, Fileno A.

IPC: G10L13/10

CPC classification number: G10L13/08 ,  G06N3/02 ,  G06N3/0445 ,  G10L13/10

Abstract: The technology relates to converting text to speech utilizing recurrent neural networks (RNNs). The recurrent neural networks may be implemented as multiple modules for determining properties of the text. In embodiments, a part-of-speech RNN module, letter-to-sound RNN module, a linguistic prosody tagger RNN module, and a context awareness and semantic mining RNN module may all be utilized. The properties from the RNN modules are processed by a hyper-structure RNN module that determine the phonetic properties of the input text based on the outputs of the other RNN modules. The hyper-structure RNN module may generate a generation sequence that is capable of being converting to audible speech by a speech synthesizer. The generation sequence may also be optimized by a global optimization module prior to being synthesized into audible speech.

Abstract translation: 该技术涉及使用循环神经网络（RNN）将文本转换为语言。 循环神经网络可以被实现为用于确定文本属性的多个模块。 在实施例中，可以使用语音RNN模块，字母对声音RNN模块，语言韵律标签器RNN模块和上下文感知和语义挖掘RNN模块。 来自RNN模块的属性由超结构RNN模块处理，该RNN模块基于其他RNN模块的输出来确定输入文本的语音属性。 超结构RNN模块可以生成能够由语音合成器转换成可听话音的生成序列。 生成序列还可以在合成为可听话音之前由全局优化模块进行优化。

公开(公告)号：WO2015191651A1

公开(公告)日：2015-12-17

申请号：PCT/US2015/034993

申请日：2015-06-10

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： ZHAO, Pei ,  YAO, Kaisheng ,  LEUNG, Max ,  HWANG, Mei-Yuh ,  ZHAO, Sheng ,  YAN, Bo ,  ZWEIG, Geoffrey ,  ALLEVA, Fileno A.

IPC: G10L13/04

CPC classification number: G10L13/08 ,  G06N3/02 ,  G06N3/0445 ,  G10L13/04

Abstract: The technology relates to performing letter-to-sound conversion utilizing recurrent neural networks (RNNs). The RNNs may be implemented as RNN modules for letter-to-sound conversion. The RNN modules receive text input and convert the text to corresponding phonemes. In determining the corresponding phonemes, the RNN modules may analyze the letters of the text and the letters surrounding the text being analyzed. The RNN modules may also analyze the letters of the text in reverse order. The RNN modules may also receive contextual information about the input text. The letter-to-sound conversion may then also be based on the contextual information that is received. The determined phonemes may be utilized to generate synthesized speech from the input text.

Abstract translation: 该技术涉及利用循环神经网络（RNN）执行字母到声音转换。 RNN可以被实现为用于字母到声音转换的RNN模块。 RNN模块接收文本输入并将文本转换为相应的音素。 在确定相应的音素时，RNN模块可以分析文本的字母和正在分析的文本周围的字母。 RNN模块还可以以相反的顺序分析文本的字母。 RNN模块还可以接收关于输入文本的上下文信息。 然后，字母对声音的转换也可以基于所接收的上下文信息。 所确定的音素可用于从输入文本生成合成语音。