Robert Boschgmbh

Zhengyu Zhou

Zhe Feng

Xiaoyang Gao

Kui Xu

Pongtep Angkititrakul

HyeongSik Kim

Chin-Hui Lee

Marcellino Gemelli

Xiaowei Zhou

William Ma

In Gyu Choi

Kehuang Li

Xuchen Song

Nicholas Feffer

Tobias Menne

Nan Tian

Rami Botros

Jiajing Guo

The systems and methods described herein are related to a new speech understanding system for domain-specific voice interaction. The systems and methods described herein combine the automatic correction of an automatic speech recognition errors with a natural language understanding model in a way that optimizes the recognition and understanding of a received speech input. The systems and methods described herein may further support out-of-domain detection or domain classification by jointly learning and/or performing automatic speech recognition error correction and domain-related classification. Through the joint learning with automatic speech recognition error correction, the out-of-domain detection or domain classification may be conducted based on a plurality of possible speech recognition results with shared feature inputs and shared neural layers. The systems and methods described herein may achieve robust performance with high computational efficiency.

System for enhancing speech understanding with effective and efficient integration of automated speech recognition error correction, out-of-domain detection, and/or domain classification

A computer-implemented method includes receiving one or more word embedding vectors in response to data indicative of one or more words. The method also includes utilizing a first recurrent neural network, outputting one or more intent representation vectors utilizing the one or more word embedding vectors; utilizing a second recurrent neural network, outputting one or more slot representation vectors utilizing the one or more intent representation vectors and the one or more word embedding vectors; and utilizing at least an additional third recurrent neural network and a fourth recurrent neural network, outputting a sentence intent and a slot label based on the one or more word embedding vectors, the one or more intent representation vectors, or the one or more slot representation vectors from either the first or second recurrent neural network.

System and method for a natural language understanding system based on iterative intent detection and slot filling neural layers

A system including a user interface that includes a processor in communication with a display and an input interface, the processor programmed to output on the display the user interface including a keyboard layout, wherein the keyboard layout includes at least a keyboard includes a collection of characters, in response to a first input from the input interface, output a first portion of the keyboard layout associated with a first subset of characters of the keyboard layout, wherein the first subset does not include all of the characters, in response to a second input from the input interface, select a second subset of characters, wherein the second subset of characters is from and include less characters than the first subset of characters and the second subset includes two or more characters, and output a character on a text field associated with the user interface based on the selection of the second subset.

System and method for coarse and fine selection keyboard user interfaces

A smart mask includes a main body having a back frame and a front cover. The back frame and the front cover each include an opening that is aligned with the mask wearer's mouth when worn. The front cover and back frame may be detachable from one another, or a single piece. A microphone is provided in the main body, as well as a speaker. A processor located in the main body is connected to the microphone and the speaker, and is configured to enhance the speech of the mask wearer. In particular, the processor receives audio signals representing a transformation of a spoken utterance of the wearer, processes the audio signals to enhance the speech, and then outputs the enhanced speech to the speaker. This helps other people better understand what the mask wearer is saying.

Smart mask and smart mask system

A framework ranks multiple hypotheses generated by one or more ASR engines for each input speech utterance. The framework jointly implements ASR improvement and NLU. It makes use of NLU related knowledge to facilitate the ranking of competing hypotheses, and outputs the top-ranked hypothesis as the improved ASR result together with the NLU results of the speech utterance. The NLU results include intent detection results and the slot filling results.

Speech recognition using natural language understanding related knowledge via deep feedforward neural networks

A dialog system and a method of using the dialog system is disclosed. The method may comprise: receiving audible human speech from a user; determining that the audible human speech comprises sarcasm information; providing an input to a neural network, wherein the input comprises speech data input associated with the audible human speech, an embedding vector associated with the sarcasm information, and a one-hot vector; and based on the input, determining an audible response to the human speech.

Sarcasm-sensitive spoken dialog system

A method for ranking candidate speech recognition results includes generating, with a controller, a plurality of feature vectors for the candidate speech recognition results, each feature vector including one or more of trigger pair features, a confidence score feature, and word-level features. The method further includes providing the plurality of feature vectors as inputs to a neural network, generating a plurality of ranking scores corresponding to the plurality of feature vectors for the plurality of candidate speech recognition results based on an output layer of the neural network, and operating the automated system using the candidate speech recognition result in the plurality of candidate speech recognition results corresponding to a highest ranking score in the plurality of ranking scores as input.

System and method for ranking of hybrid speech recognition results with neural networks

A method for sign language recognition includes receiving, with an input device, an input based on a plurality of hand movements and postures of a user that correspond to a sequence of signs, extracting a plurality of features from the input corresponding to the plurality of hand movements and postures, identifying, a start of the sequence of signs in the input based on a first set of features in the plurality of features and a first Hidden Markov Model (HMM) stored in the memory and identifying a first sign in the input based on a second set of features in the plurality of features and a second HMM stored in the memory. The method also includes generating an output corresponding to the first sign from the input.

System and method for automated sign language recognition

A method for automated speech recognition includes generating first and second pluralities of candidate speech recognition results corresponding to audio input data using a first general-purpose speech recognition engine and a second domain-specific speech recognition engine, respectively. The method further includes generating a third plurality of candidate speech recognition result including a plurality of words included in one of the first plurality of speech recognition results and at least one word included in another one of the second plurality of speech recognition results, ranking the third plurality of candidate speech recognition results using a pairwise ranker to identify a highest ranked candidate speech recognition result, and operating the automated system using the highest ranked speech recognition result as an input from the user.

Growing community of inventors

Fremont, CA, United States of America

Zhengyu Zhou