Intel Corporation

Munir Georges

Tobias Bocklet

Joachim Hofer

Jonathan J Huang

Josef Bauer

Wenda Chen

Piotr Rozen

Jakub Nowicki

Krzysztof Czarnowski

Moinul Islam Al-Mamun

Methods, apparatus, systems and articles of manufacture for recognizing speech are disclosed. An example system includes one or more processors to execute instructions to: identify a plurality of phonemes in a speech signal; perform a comparison of a subset of the phonemes to a phonetic string, the phonetic string representative of at least a portion of a wake up phrase; determine if one or more of the phonemes of the subset correspond to the wake up phrase based on the comparison; and generate a hypothesis of a command included in the speech signal by excluding the wake up phrase when one or more of the phonemes of the subset correspond to the wake up phrase or a portion of the wake up phrase.

Automatic speech recognition with filler model processing

An embodiment of a spoken intent detection device includes technology to detect a phrase in an electronic representation of an audio stream based on a pre-defined vocabulary, associate a time stamp with the detected phrase, and classify a spoken intent based on a sequence of detected phrases and the respective associated time stamps. Other embodiments are disclosed and claimed.

Time asynchronous spoken intent detection

Spoken language understanding techniques include training a dynamic neural network mask relative to a static neural network using only post-deployment training data such that the mask zeroes out some of the weights of the static neural network and allows some other weights to pass through and applying a dynamic neural network corresponding to the masked static neural network to input queries to identify outputs for the queries.

On-device neural network adaptation with binary mask learning for language understanding systems

An automatic speech recognition (ASR) system includes a memory configured to store a filler model. The filler model includes one or more phonetic strings corresponding to one or more portions of a wake up phrase. The ASR system also includes one or more processors operatively coupled to the memory and configured to analyze a speech signal with the filler model to determine whether the speech signal includes the wake up phrase or any portion of the wake up phrase. The one or more processors are also configured to generate, based on the analysis, a hypothesis of underlying speech included in the speech signal. The hypothesis excludes the wake up phrase or any portion of the wake up phrase included in the speech signal.

An embodiment of a wake-on-intent speech recognition device includes technology to detect one or more keywords in a digital representation of a spoken natural language utterance, determine an intent of the spoken natural language utterance based on the detected keywords, and provide the spoken natural language utterance to a speech recognition and interpretation system if the determined intent is to further process the spoken natural language utterance. Other embodiments are disclosed and claimed.

Spoken keyword detection based utterance-level wake on intent system

Herein is disclosed a sensor data evaluation system comprising one or more first sensors, configured to deliver first sensor data to a first sensor frontend; the first sensor frontend, configured to generate a first sensor frontend output corresponding to the first sensor data and to deliver the first sensor frontend output to a second sensor frontend; and the second sensor frontend, configured to receive second sensor data from one or more second sensors; and modify a second sensor parameter based at least on the first sensor frontend output.

Working condition classification for sensor fusion

Techniques are disclosed for client-side analysis of audio samples to identify one or more characteristics associated with captured audio. The client-side analysis may then allow a user device, e.g., a smart phone, laptop computer, in-car infotainment system, and so on, to provide the one or more identified characteristics as configuration data to a voice recognition service at or shortly after connection with the same. In turn, the voice recognition service may load one or more recognition components, e.g., language models and/or application modules/engines, based on the received configuration data. Thus, latency may be reduced based on the voice recognition engine having 'hints' that allow components to be loaded without necessarily having to process audio samples first. The reduction of latency may reduce processing time relative to other approaches to voice recognitions systems that exclusively perform server-side context recognition/classification.

Techniques for client-side speech domain detection using gyroscopic data and a system using the same

An embodiment of a speech endpoint detector apparatus may include a speech detector to detect a presence of speech in an electronic speech signal, a pause duration measurer communicatively coupled to the speech detector to measure a duration of a pause following a period of detected speech, an end of utterance detector communicatively coupled to the pause duration measurer to detect if the pause measured following the period of detected speech is greater than a pause threshold corresponding to an end of an utterance, and a pause threshold adjuster to adaptively adjust the pause threshold corresponding to an end of an utterance based on stored pause information. Other embodiments are disclosed and claimed.

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Kehl, Germany

Munir Georges