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    Methods and apparatuses for automatic speech recognition
    51.
    发明授权
    Methods and apparatuses for automatic speech recognition 有权

    公开(公告)号：US10283110B2

    公开(公告)日：2019-05-07

    申请号：US15204642

    申请日：2016-07-07

    Applicant: Apple Inc.

    Inventor： Jerome R. Bellegarda

    IPC: G10L15/06 ,  G10L15/08 ,  G10L15/187 ,  G10L15/32 ,  G10L15/14

    Abstract: Exemplary embodiments of methods and apparatuses for automatic speech recognition are described. First model parameters associated with a first representation of an input signal are generated. The first representation of the input signal is a discrete parameter representation. Second model parameters associated with a second representation of the input signal are generated. The second representation of the input signal includes a continuous parameter representation of residuals of the input signal. The first representation of the input signal includes discrete parameters representing first portions of the input signal. The second representation includes discrete parameters representing second portions of the input signal that are smaller than the first portions. Third model parameters are generated to couple the first representation of the input signal with the second representation of the input signal. The first representation and the second representation of the input signal are mapped into a vector space.

    Entropy-guided text prediction using combined word and character n-gram language models
    52.
    发明授权
    Entropy-guided text prediction using combined word and character n-gram language models 有权

    公开(公告)号：US10078631B2

    公开(公告)日：2018-09-18

    申请号：US14713420

    申请日：2015-05-15

    Applicant: Apple Inc.

    Inventor： Jerome R. Bellegarda

    IPC: G06F17/27

    CPC classification number: G06F17/276

    Abstract: Systems and processes are disclosed for predicting words in a text entry environment. Candidate words and probabilities associated therewith can be determined by combining a word n-gram language model and a character m-gram language model. Based on entered text, candidate word probabilities from the word n-gram language model can be integrated with the corresponding candidate character probabilities from the character m-gram language model. A reduction in entropy can be determined from integrated candidate word probabilities before entry of the most recent character to integrated candidate word probabilities after entry of the most recent character. If the reduction in entropy exceeds a predetermined threshold, candidate words with high integrated probabilities can be displayed or otherwise made available to the user for selection. Otherwise, displaying candidate words can be deferred (e.g., pending receipt of an additional character from the user leading to reduced entropy in the candidate set).

    Systems And Methods For Character Sequence Recognition With No Explicit Segmentation
    55.
    发明申请
    Systems And Methods For Character Sequence Recognition With No Explicit Segmentation 审中-公开
    没有显式分割的字符序列识别的系统和方法

    公开(公告)号：US20150347860A1

    公开(公告)日：2015-12-03

    申请号：US14292781

    申请日：2014-05-30

    Applicant: APPLE INC.

    Inventor： Ueli Meier ,  Ryan S. Dixon ,  Karl M. Groethe ,  Jerome R. Bellegarda

    IPC: G06K9/18 ,  G06K9/62 ,  G06K9/32 ,  G06K9/72

    CPC classification number: G06K9/72 ,  G06K9/2081 ,  G06K9/3241 ,  G06K9/3258 ,  G06K9/3275 ,  G06K9/4628

    Abstract: Differing embodiments of this disclosure may be employed to perform character sequence recognition with no explicit character segmentation. According to some embodiments, the character sequence recognition process may comprise generating a predicted character sequence for a first representation of a first image comprising a first plurality of pixels by: sliding a Convolutional Neural Network (CNN) classifier over the first representation of the first image one pixel position at a time until reaching an extent of the first representation of the first image; recording a likelihood value for each of k potential output classes at each pixel position, wherein one of the k potential output classes comprises a background class; determining a sequence of most likely output classes at each pixel position; decoding the sequence by removing identical consecutive output class determinations and background class determinations from the determined sequence; and validating the decoded sequence using one or more predetermined heuristics.

    Abstract translation: 可以采用本公开的不同实施例来执行字符序列识别，而不需要明确的字符分割。 根据一些实施例，字符序列识别过程可以包括：通过以下步骤生成包括第一多个像素的第一图像的第一表示的预测字符序列：在第一图像的第一表示上滑动卷积神经网络（CNN）分类器 一次一个像素位置，直到达到第一图像的第一表示的程度; 在每个像素位置记录k个电位输出类别中的每一个的似然值，其中所述k个电位输出类别中的一个包括背景类别; 确定每个像素位置处的最可能的输出类别的序列; 通过从确定的序列中去除相同的连续输出类确定和背景类确定来对序列进行解码; 以及使用一个或多个预定的启发式验证所述解码序列。

    Credit Card Auto-Fill
    56.
    发明申请
    Credit Card Auto-Fill 有权
    信用卡自动填充

    公开(公告)号：US20150347859A1

    公开(公告)日：2015-12-03

    申请号：US14292763

    申请日：2014-05-30

    Applicant: Apple Inc.

    Inventor： Ryan S. Dixon ,  Ueli Meier ,  Karl M. Groethe ,  Jerome R. Bellegarda ,  Giulia M. Pagallo

    IPC: G06K9/18 ,  G06K9/32 ,  G06K9/72 ,  G06K9/20

    CPC classification number: G06K9/186 ,  G06K9/2081 ,  G06K9/228 ,  G06K9/325 ,  G06K9/72

    Abstract: Differing embodiments of this disclosure may employ one or all of the several techniques described herein to perform credit card recognition using electronic devices with integrated cameras. According to some embodiments, the credit card recognition process may comprise: obtaining a first representation of a first image, wherein the first representation comprises a first plurality of pixels; identifying a first credit card region within the first representation; extracting a first plurality of sub-regions from within the identified first credit card region, wherein a first sub-region comprises a credit card number, wherein a second sub-region comprises an expiration date, and wherein a third sub-region comprises a card holder name; generating a predicted character sequence for the first, second, and third sub-regions; and validating the predicted character sequences for at least the first, second, and third sub-regions using various credit card-related heuristics, e.g., expected character sequence length, expected character sequence format, and checksums.

    Abstract translation: 本公开的不同实施例可以采用本文所描述的几种技术中的一种或全部，以使用具有集成照相机的电子设备执行信用卡识别。 根据一些实施例，信用卡识别过程可以包括：获得第一图像的第一表示，其中第一表示包括第一多个像素; 识别所述第一表示内的第一信用卡区域; 从所识别的第一信用卡区域内提取第一多个子区域，其中第一子区域包括信用卡号码，其中第二子区域包括到期日期，并且其中第三子区域包括卡 持有人名称; 生成第一，第二和第三子区域的预测字符序列; 以及使用各种信用卡相关启发式，例如预期字符序列长度，期望字符序列格式和校验和来验证至少第一，第二和第三子区域的预测字符序列。

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