Qualcomm Incorporated

Mingu Lee

Kyu Woong Hwang

Jinkyu Lee

Wonil Chang

Jae-won Choi

Yin Huang

Haijun Zhao

Byeonggeun Kim

Jinseok Lee

Ruowei Wang

Janghoon Cho

Dooyong Sung

Yeonseok Kim

Hye Jin Jang

Certain aspects of the present disclosure are generally directed to apparatus and techniques for event state detection. One example method generally includes receiving a plurality of sensor signals at a computing device, determining, at the computing device, probabilities of sub-event states based on the plurality of sensor signals using an artificial neural network for each of a plurality of time intervals, and detecting, at the computing device, the event state based on the probabilities of the sub-event states via a state sequence model.

Vehicle entry detection

System and method for operating an always-on ASR (automatic speech recognition) system by selecting target keywords and continuously detecting the selected target keywords in voice commands in a mobile device are provided. In the mobile device, a processor is configured to collect keyword candidates, collect usage frequency data for keywords in the keyword candidates, collect situational usage frequency data for the keywords in the keyword candidates, select target keywords from the keyword candidates based on the usage frequency data and the situational usage frequency data, and detect one or more of the target keywords in a voice command using continuous detection of the target keywords.

Target keyword selection

A method for operating a neural network includes receiving an input sequence at an encoder. The input sequence is encoded to produce a set of hidden representations. Attention-heads of the neural network calculate attention weights based on the hidden representations. A context vector is calculated for each attention-head based on the attention weights and the hidden representations. Each of the context vectors correspond to a portion of the input sequence. An inference is output based on the context vectors.

Orthogonally constrained multi-head attention for speech tasks

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Hwaseong, South Korea

Mingu Lee