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公开(公告)号：US09484021B1
公开(公告)日：2016-11-01
申请号：US14673089
申请日：2015-03-30
Applicant: Amazon Technologies, Inc.
Abstract: Automatic speech recognition (ASR) processing including a two-stage configuration. After ASR processing of an incoming utterance where the ASR outputs an N-best list including multiple hypotheses, a first stage determines whether to execute a command associated with one of the hypotheses or whether to output some of the hypotheses of the N-best list for disambiguation. A second stage determines what hypotheses should be included in the disambiguation choices. A first machine learning model is used at the first stage and a second machine learning model is used at the second stage. The multi-stage configuration allows for reduced speech processing errors as well as a reduced number of utterances sent for disambiguation, which thus improves the user experience.
Abstract translation: 自动语音识别（ASR）处理包括两级配置。 在ASR处理ASR输出包括多个假设的N最佳列表的传入话语的ASR处理之后，第一阶段确定是否执行与假设之一相关联的命令，或者是否输出N最佳列表的一些假设 消歧 第二阶段确定在消歧选择中应包含哪些假设。 在第一阶段使用第一机器学习模型，在第二阶段使用第二机器学习模型。 多级配置允许减少语音处理错误以及为消除歧义而发送的减少的话语数量，从而改善用户体验。
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Patent Agency Ranking
公开(公告)号：US11978445B1
公开(公告)日：2024-05-07
申请号：US17217994
申请日：2021-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Edward Bueche , Amaury Gutierrez Acosta , Francois Mairesse , Yun Suk Paik , Anmol Tiwari , Tao Ye
IPC: G10L15/22
CPC classification number: G10L15/22
Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a model trained to identify a dialog act that is most likely to lead to a desired outcome. Upon receiving an invocation to begin a conversation, a score indicative of a level of confidence that the conversation will have a successful outcome is determined, and a dialog act is selected based on the score. Subsequently, at each turn of the conversation, the score is updated or a new score is calculated, and a dialog act is selected based on the updated or new score. Confidence scores are calculated based on input features that are determined based on the user who uttered the invocation or responses to dialog acts, as well as a context of the conversation, and provided to a linear model or a machine learning model as inputs.
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公开(公告)号：US09558740B1
公开(公告)日：2017-01-31
申请号：US14673343
申请日：2015-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Francois Mairesse , Paul Frederick Raccuglia , Shiv Naga Prasad Vitaladevuni , Simon Peter Reavely
Abstract: Automatic speech recognition (ASR) processing including a feedback configuration to allow for improved disambiguation between ASR hypotheses. After ASR processing of an incoming utterance where the ASR outputs an N-best list including multiple hypotheses, the multiple hypotheses are passed downstream for further processing. The downstream further processing may include natural language understanding (NLU) or other processing to determine a command result for each hypothesis. The command results are compared to determine if any hypotheses of the N-best list would yield similar command results. If so, the hypothesis(es) with similar results are removed from the N-best list so that only one hypothesis of the similar results remains in the N-best list. The remaining non-similar hypotheses are sent for disambiguation, or, if only one hypothesis remains, it is sent for execution.
Abstract translation: 自动语音识别（ASR）处理包括反馈配置，以便改善ASR假设之间的歧义。 在ASR处理ASR输出包含多个假设的N最佳列表的传入语句之后，多个假设被传递到下游进行进一步处理。 下游进一步处理可以包括自然语言理解（NLU）或其他处理以确定每个假设的命令结果。 比较命令结果以确定N最佳列表的任何假设是否会产生类似的命令结果。 如果是这样，具有类似结果的假设从N最佳列表中删除，因此只有一个类似结果的假设保留在N最佳列表中。 发送剩余的非类似假设以消除歧义，或者如果只剩下一个假设，则将其发送执行。
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公开(公告)号：US11899714B1
公开(公告)日：2024-02-13
申请号：US16144974
申请日：2018-09-27
Applicant: Amazon Technologies, Inc.
Inventor： Edward Bueche , Francois Mairesse , Amina Shabbeer , Warren D. Freitag , Jonathan Pollack , Charles Lee Thorp
IPC: G06F16/635 , G06F16/68 , G06F3/16 , G10L15/22 , G06F16/2453
CPC classification number: G06F16/686 , G06F3/16 , G06F16/24542 , G06F16/635 , G10L15/22
Abstract: Voice data from a current conversation between a user and a voice-controlled user device can be used to determine a search constraint for searching a database. Other search constraints can be determined based at least in part on the current conversation, a previous conversation, and/or a previous action. Properties can be associated with the search constraints. Once the search constraints have been determined, a plurality of search query plans is determined and a first search query plan is executed to query the database.
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公开(公告)号：US11776542B1
公开(公告)日：2023-10-03
申请号：US17217943
申请日：2021-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Edward Bueche , Francois Mairesse , Torbjorn Vik , Tao Ye
CPC classification number: G10L15/22 , G06N20/00 , G10L2015/088 , G10L2015/223
Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a machine learning model trained to identify a dialog act that is most likely to lead to a desired outcome. When an invocation is received from a user, a context of the invocation is determined, and a pool of dialog acts is scored based on the context by a machine learning model. Dialog acts are selected from the pool and presented to the user in accordance with a randomization policy. Data regarding the dialog acts and their success in achieving a desired outcome is used to train one or more machine learning models to select dialog acts in response to invocations.
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公开(公告)号：US10283111B1
公开(公告)日：2019-05-07
申请号：US15382874
申请日：2016-12-19
Applicant: Amazon Technologies, Inc.
Inventor： Francois Mairesse , Paul Frederick Raccuglia , Shiv Naga Prasad Vitaladevuni , Simon Peter Reavely
Abstract: Automatic speech recognition (ASR) processing including a feedback configuration to allow for improved disambiguation between ASR hypotheses. After ASR processing of an incoming utterance where the ASR outputs an N-best list including multiple hypotheses, the multiple hypotheses are passed downstream for further processing. The downstream further processing may include natural language understanding (NLU) or other processing to determine a command result for each hypothesis. The command results are compared to determine if any hypotheses of the N-best list would yield similar command results. If so, the hypothesis(es) with similar results are removed from the N-best list so that only one hypothesis of the similar results remains in the N-best list. The remaining non-similar hypotheses are sent for disambiguation, or, if only one hypothesis remains, it is sent for execution.
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