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    • 4. 发明申请
    • DETECTING THAT A MOBILE DEVICE IS RIDING WITH A VEHICLE
    • 检测移动设备是否与车辆骑行
    • WO2013040493A1
    • 2013-03-21
    • PCT/US2012/055622
    • 2012-09-14
    • QUALCOMM INCORPORATEDGROKOP, Leonard, HenryDHINGRA, Bhuwan
    • GROKOP, Leonard, HenryDHINGRA, Bhuwan
    • H04M1/725H04W4/02
    • G01P13/00H04M1/72569H04M2250/12H04W4/027
    • Systems and methods herein enable a mobile device to detect that a user is traveling in association with a vehicle based at least on motion data. In some embodiments, accelerometer data is used. Motion data is leveraged in combination with various observations regarding vehicular movement to determine whether or not a mobile device is located in or on the vehicle. For instance, before entering the state of vehicular movement, it can be determined that the user is first in a walking state (e.g., walking to the car, bus, etc., and entering it). Likewise, after exiting the state of vehicular movement, the user re-enters the walking state (e.g., after stepping out of the car, bus, etc., the user again begins walking). Further, it can be determined that when the user is in the walking state, the accelerometer signals appear different to any accelerometer signals seen in the vehicular movement state.
    • 这里的系统和方法使得移动设备能够至少基于运动数据来检测用户与车辆相关联地行驶。 在一些实施例中,使用加速度计数据。 运动数据与关于车辆运动的各种观察结合使用,以确定移动设备是否位于车辆中或车辆上。 例如,在进入车辆移动状态之前,可以确定用户首先处于行走状态(例如,走到汽车,公共汽车等,并进入车辆)。 同样地,在退出车辆移动状态之后,用户重新进入步行状态(例如,在步行出车,公车等之后再次开始行走)。 此外,可以确定当用户处于行走状态时,加速度计信号看起来与在车辆运动状态中看到的任何加速度计信号不同。
    • 7. 发明申请
    • LEARNING SPEECH MODELS FOR MOBILE DEVICE USERS
    • 学习移动设备用户的语音模型
    • WO2013006489A1
    • 2013-01-10
    • PCT/US2012/045101
    • 2012-06-29
    • QUALCOMM INCORPORATEDGROKOP, Leonard, HenryNARAYANAN, Vidya
    • GROKOP, Leonard, HenryNARAYANAN, Vidya
    • G10L15/06
    • G10L15/063G06N7/005G10L2015/0631
    • Techniques are provided to recognize a speaker's voice. In one embodiment, received audio data may be separated into a plurality of signals. For each signal, the signal may be associated with value/s for one or more features (e.g., Mel-Frequency Cepstral coefficients). The received data may be clustered (e.g., by clustering features associated with the signals). A predominate voice cluster may be identified and associated with a user. A speech model (e.g., a Gaussian Mixture Model or Hidden Markov Model) may be trained based on data associated with the predominate cluster. A received audio signal may then be processed using the speech model to, e.g.,: determine who was speaking; determine whether the user was speaking; determining whether anyone was speaking; and/or determine what words were said. A context of the device or the user may then be inferred based at least partly on the processed signal.
    • 提供技术来识别扬声器的声音。 在一个实施例中,所接收的音频数据可以被分成多个信号。 对于每个信号,信号可以与一个或多个特征(例如,梅尔频率倒频谱系数)的值/ s相关联。 接收到的数据可以被聚类(例如,通过聚集与信号相关联的特征)。 可以识别主要的语音群集并且与用户相关联。 可以基于与主要簇相关联的数据来训练语音模型(例如,高斯混合模型或隐马尔可夫模型)。 然后可以使用语音模型来处理接收到的音频信号,例如:确定谁在说话; 确定用户是否在说话; 确定是否有人在说话; 和/或确定说什么话。 可以至少部分地基于经处理的信号来推断设备或用户的上下文。