Intel Corporation

Svox Ag

Georg Stemmer

Joachim Hofer

Josef G Bauer

Tobias Bocklet

Piotr Rozen

Munir Nikolai Alexander Georges

Saurin Shah

Francis M Tharappel

Binuraj K Ravindran

Prabhakar R Datta

Tomasz Dorau

Maciej Muchlinski

Michael E Deisher

Da-Ming Chiang

David Pearce

Piotr Majcher

Lukasz Kurylo

Ilke Demir

Andrzej Mialkowski

Satyam Srivastava

Sebastian Czyryba

Krzysztof Czarnowski

Tomasz Szmelczynski

Lukasz M Malinowski

David J Trawick

Praful Mangalath

An apparatus to facilitate deepfake detection models utilizing subject-specific libraries is disclosed. The apparatus includes one or more processors to store a plurality of deepfake detection models corresponding to a plurality of subjects of interest; receive a query to identify whether data pertaining to a target subject of interest is a deepfake, the target subject of interest comprised in the plurality of subjects of interest and associated with a subject identifier (ID); identify a deepfake detection model corresponding to the subject ID; extract features for deepfake detection from the data; input the extracted features to the identified deepfake detection model corresponding to the subject ID; and responsive to an output of the deepfake detection model exceeding a determined deepfake threshold, generate a notification, in response to the query, indicating a possible deepfake attack corresponding to the target subject of interest.

Deepfake detection models utilizing subject-specific libraries

This disclosure describes systems, methods, and devices related to presenting video conferencing virtual seating arrangements. A method may include generating a first similarity score indicative of a first similarity between a first voice of a first virtual meeting user and a second voice of a second virtual meeting user; generating a second similarity score indicative of a second similarity between the first voice of the first virtual meeting user and a third voice of a third virtual meeting user; determining, based on the first similarity score and the second similarity score, a similarity loss for a virtual seating arrangement; determining that the similarity loss is a minimum similarity loss of respective similarity losses for different virtual seating arrangements; generating presentation data, for the virtual meeting, including virtual representations of the virtual meeting users arranged based on the virtual seating arrangement; and presenting the presentation data.

Enhanced spatial audio-based virtual seating arrangements

An example apparatus for recognizing speech includes an audio receiver to receive a stream of audio. The apparatus also includes a key phrase detector to detect a key phrase in the stream of audio. The apparatus further includes a model adapter to dynamically adapt a model based on the detected key phrase. The apparatus also includes a query recognizer to detect a voice query following the key phrase in a stream of audio via the adapted model.

Adaptively recognizing speech using key phrases

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 mechanism is described for facilitating continuous topic detection and adaption in audio environments, according to one embodiment. A method of embodiments, as described herein, includes detecting a term relating to a topic in an audio input received from one or more microphones of the computing device including a voice-enabled device; analyzing the term based on the topic to determine an action to be performed by the computing device; and triggering an event to facilitate the computing device to perform the action consistent with the term and the topic.

Continuous topic detection and adaption in audio environments

Techniques related to implementing neural networks for speech recognition systems are discussed. Such techniques may include processing a node of the neural network by determining a score for the node as a product of weights and inputs such that the weights are fixed point integer values, applying a correction to the score based on a correction value associated with at least one of the weights, and generating an output from the node based on the corrected score.

Fixed point integer implementations for neural networks

Techniques are provided for wake-on-voice (WOV) key-phrase enrollment. A methodology implementing the techniques according to an embodiment includes generating a WOV key-phrase model based on identification of the sequence of sub-phonetic units of a user-provided key-phrase. The WOV key-phrase model is employed by a WOV processor for detection of the user spoken key-phrase and triggering operation of an automatic speech recognition (ASR) processor in response to the detection. The method further includes updating an ASR language model based on the user-provided key-phrase. The update includes one of embedding the WOV key-phrase model into the ASR language model, converting sub-phonetic units of the WOV key-phrase model and embedding the converted WOV key-phrase model into the ASR language model, or generating an ASR key-phrase model by applying a phoneme-syllable based statistical language model to the user-provided key-phrase and embedding the generated ASR key-phrase model into the ASR language model.

Dynamic enrollment of user-defined wake-up key-phrase for speech enabled computer system

Techniques are provided for reducing the latency of automatic speech recognition using hypothesis score trend analysis. A methodology implementing the techniques according to an embodiment includes generating complete-phrase hypotheses and partial-phrase hypotheses, along with associated likelihood scores, based on a segment of speech. The method also includes selecting the complete-phrase hypothesis associated with the highest of the complete-phrase hypotheses likelihood scores, and selecting the partial-phrase hypothesis associated with the highest of the partial-phrase hypotheses likelihood scores. The method further includes calculating a relative likelihood score based on a ratio of the likelihood score associated with the selected complete-phrase hypothesis to the likelihood score associated with the selected partial-phrase hypothesis. The method further includes calculating a trend of the relative likelihood score as a function of time and identifying an endpoint of the speech based on a determination that the trend does not decrease over a selected time period.

Score trend analysis for reduced latency automatic speech recognition

A system, article, and method include techniques of automatic speech recognition using posterior confidence scores.

Method and system of automatic speech recognition using posterior confidence scores

A language model is modified for a local speech recognition system using remote speech recognition sources. In one example, a speech utterance is received. The speech utterance is sent to at least one remote speech recognition system. Text results corresponding to the utterance are received from the remote speech recognition system. A local text result is generated using local vocabulary. The received text results and the generated text result are compared to determine words that are out of the local vocabulary and the local vocabulary is updated using the out of vocabulary words.

Language model modification for local speech recognition systems using remote sources

Techniques are provided for calculating reset parameters for recurrent neural networks (RNN). A methodology implementing the techniques according to an embodiment includes generating a sequence of statistics. The calculation of each statistic is based on outputs of an RNN that is periodically re-initialized at a selected RNN reset time such that each of the calculated statistics is associated with a unique RNN reset time selected from a pre-determined range of reset times. The method further includes analyzing the sequence to identify a maximum interval during which the sequence remains relatively constant. The method further includes selecting a reset time parameter and reset context duration parameter, for re-initialization of the RNN during operation. The reset time parameter is based on the duration of the identified maximum interval and the reset context duration parameter is based on a time associated with the starting point of the identified maximum interval.

Statistical-analysis-based reset of recurrent neural networks for automatic speech recognition

A computer-implemented method of speech recognition comprises forming a weighted finite state transducer (WFST) having nodes associated with states and interconnected by arcs, and to identify at least one word or word sequence hypothesis, identifying multiple sub-graphs on the WFST, each sub-graph having the same arrangement of multiple states and at least one arc, and propagating tokens in parallel through the sub-graphs, where each sub-graph is stored as a supertoken each having an array of tokens.

System and method of automatic speech recognition using parallel processing for weighted finite state transducer-based speech decoding

Technologies for improved keyword spotting are disclosed. A compute device may capture speech data from a user of the compute device, and perform automatic speech recognition on the captured speech data. The automatic speech recognition algorithm is configured to both spot keywords as well as provide a full transcription of the captured speech data. The automatic speech recognition algorithm may preferentially match the keywords compared to similar words. The recognized keywords may be used to improve parsing of the transcribed speech data or to improve an assistive agent in holding a dialog with a user of the compute device.

Technologies for improved keyword spotting

A method for context-aware query recognition in an electronic device includes receiving user speech from an input device. A speech signal is generated from the user speech. It is determined if the speech signal includes an action to be performed and if the electronic device is the intended recipient of the user speech. If the recognized speech signal include the action and the intended recipient of the user speech is the electronic device, a command is generated for the electronic device to perform the action.

Growing community of inventors

Munich, Germany

Georg Stemmer