Intel Corporation

Siemens Aktiengesellschaft

Bayerische Motoren Werke Aktiengesellschaft

Josef G Bauer

Georg Stemmer

Joachim Hofer

Tobias Bocklet

Piotr Rozen

Saurin Shah

Francis M Tharappel

Binuraj K Ravindran

Prabhakar R Datta

Munir Nikolai Alexander Georges

Tomasz Dorau

Maciej Muchlinski

Dieter Kunze

Heinz Lemberger

Hans-Juergen Meltsch

Da-Ming Chiang

David Pearce

Piotr Majcher

Herbert Krause

Jochen Junkawitsch

Sebastian Czyryba

Lukasz M Malinowski

Eugen Bochskanl

David J Trawick

Praful Mangalath

Sergey Astrov

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

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 turbocharger, in particular a turbocharger for a motor vehicle, is provided. The turbocharger includes a turbine to which exhaust gases can be supplied by two fluid ducts between which a separation wall extends; a closeable fluid connection hole in the separation wall that can be closed by a plate-shaped slider between the fluid ducts. The invention further relates to a motor vehicle comprising the turbocharger.

Turbocharger comprising a slider for fluid connection

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

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

Techniques related to key phrase detection for applications such as wake on voice are discussed. Such techniques may include determining a sequence of audio units for received audio input representing a user defined key phrase, eliminating audio units from the sequence to generate a final sequence of audio units, and generating a key phrase recognition model representing the user defined key phrase based on the final sequence.

User defined key phrase detection by user dependent sequence modeling

Technologies for identifying sounds are disclosed. A sound identification device may capture sound data, and split the sound data into frames. The sound identification device may then determine an acoustic feature vector for each frame, and determine parameters based on how each acoustic feature varies over the duration of time corresponding to the frames. The sound identification device may then determine if the sound matches a pre-defined sound based on the parameters. In one embodiment, the sound identification device may be a baby monitor, and the pre-defined sound may be a baby crying.

Technologies for robust crying detection using temporal characteristics of acoustic features

A system, article, and method of automatic speech recognition with dynamic vocabularies is described herein.

Method and system of automatic speech recognition with dynamic vocabularies

Techniques related to implementing neural networks for speech recognition systems are discussed. Such techniques may include implementing frame skipping with approximated skip frames and/or distances on demand such that only those outputs needed by a speech decoder are provided via the neural network or approximation techniques.

Frame skipping with extrapolation and outputs on demand neural network for automatic speech recognition

Commercially available voice recognition systems are generally speaker-dependent, with the voice recognition system first being trained to the voice of the speaker before it can be used. A disadvantage with this method is that modified reference data has to be buffered and permanently saved in several steps when the speaker adaptation algorithm is executed, and thus requires a lot of memory space. This primarily negatively affects applications on devices with restricted processor power and limited memory space, such as mobile radio terminals for example. A method of speaker adaptation for a Hidden Markov Model based voice recognition system may address these issues. In the method, the memory space requirement and thus also the processor power required can be considerably reduced. This is achieved by using modified reference data in a speaker adaptation algorithm to adapt a new speaker to a reference speaker. The modified reference data is processed in compressed form.

Method of speaker adaptation for a hidden markov model based voice recognition system

Connection data, for example, a telephone number, and a corresponding identification, for example, the name of the telephone subscriber associated with the corresponding telephone number, is automatically extracted from a data memory and saved in a dialing directory memory.

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Munich, Germany

Josef G Bauer