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公开(公告)号：US20180286385A1
公开(公告)日：2018-10-04
申请号：US16000742
申请日：2018-06-05
Inventor： Aravind Ganapathiraju , Yingyi Tan , Felix Immanuel Wyss , Scott Allen Randal
CPC classification number: G10L15/01 , G10L2015/088
Abstract: A system and method are presented for predicting speech recognition performance using accuracy scores in speech recognition systems within the speech analytics field. A keyword set is selected. Figure of Merit (FOM) is computed for the keyword set. Relevant features that describe the word individually and in relation to other words in the language are computed. A mapping from these features to FOM is learned. This mapping can be generalized via a suitable machine learning algorithm and be used to predict FOM for a new keyword. In at least embodiment, the predicted FOM may be used to adjust internals of speech recognition engine to achieve a consistent behavior for all inputs for various settings of confidence values.
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2.发明申请公开(公告)号：US20190355348A1
公开(公告)日：2019-11-21
申请号：US16414885
申请日：2019-05-17
Inventor： Ramasubramanian Sundaram , Aravind Ganapathiraju , Yingyi Tan
Abstract: A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.
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公开(公告)号：US11195514B2
公开(公告)日：2021-12-07
申请号：US16414885
申请日：2019-05-17
Inventor： Ramasubramanian Sundaram , Aravind Ganapathiraju , Yingyi Tan
Abstract: A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.
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公开(公告)号：US10360898B2
公开(公告)日：2019-07-23
申请号：US16000742
申请日：2018-06-05
Inventor： Aravind Ganapathiraju , Yingyi Tan , Felix Immanuel Wyss , Scott Allen Randal
Abstract: A system and method are presented for predicting speech recognition performance using accuracy scores in speech recognition systems within the speech analytics field. A keyword set is selected. Figure of Merit (FOM) is computed for the keyword set. Relevant features that describe the word individually and in relation to other words in the language are computed. A mapping from these features to FOM is learned. This mapping can be generalized via a suitable machine learning algorithm and be used to predict FOM for a new keyword. In at least embodiment, the predicted FOM may be used to adjust internals of speech recognition engine to achieve a consistent behavior for all inputs for various settings of confidence values.
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