Intel Corporation

Tomasz Dorau

Tobias Bocklet

Maciej Muchlinski

Przemyslaw Sobon

Saurin Shah

Georg Stemmer

Josef G Bauer

Francis M Tharappel

Binuraj K Ravindran

Prabhakar R Datta

Przemyslaw Tomaszewski

David Pearce

Piotr Rozen

Adam Marek

Jacek Ossowski

Marcin Terpilowski

A method, system, and device are directed to audio input bit-size conversion for compatibility to audio processing systems with an expected input sample bit-size.

Method and system of audio input bit-size conversion for audio processing

Methods, apparatus, systems and articles of manufacture are disclosed for distributed automatic speech recognition. An example apparatus includes a detector to process an input audio signal and identify a portion of the input audio signal including a sound to be evaluated, the sound to be evaluated organized into a plurality of audio features representing the sound. The example apparatus includes a quantizer to process the audio features using a quantization process to reduce the audio features to generate a reduced set of audio features for transmission. The example apparatus includes a transmitter to transmit the reduced set of audio features over a low-energy communication channel for processing.

Systems and methods for energy efficient and low power distributed automatic speech recognition on wearable devices

A method and system are directed to autonomous neural network keyphrase detection and includes generating and using a multiple element state score vector by using neural network operations and without substantial use of a digital signal processor (DSP) to perform the keyphrase detection.

Method and system of neural network keyphrase detection

Key phrase detection techniques for applications such as wake on voice are discussed include performing a vectorized operation on a multiple element acoustic score vector for a current time instance including a single state rejection model score and scores for a multiple state key phrase model and a multiple element state score vector for a previous time instance including a previous state score for the single state rejection model and previous state scores for the multiple state key phrase model to generate a multiple element score summation vector and a second vectorized operation on the multiple element score summation vector to determine a multiple element state score vector for the current time instance. The multiple element state score vector for the current time instance may then be evaluated to determine whether received audio input includes a key phrase corresponding to the multiple state key phrase model.

Linear scoring for low power wake on voice

Techniques related to key phrase detection for applications such as wake on voice are discussed. Such techniques may include intermediate scoring of a state or states of a key phrase model and/or a backward transition or rejection loopback from a state of the key phrase model to a rejection model to reduce false accepts based on received utterances.

Intermediate scoring and rejection loopback for improved key phrase detection

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Gdansk, Poland

Tomasz Dorau