Robert Boschgmbh

Pongtep Angkititrakul

Fuliang Weng

Zhongnan Shen

HyeongSik Kim

Gengyan Bei

Xiaowei Zhou

Samarjit Das

Xiaoyang Gao

Zhengyu Zhou

Raphael Schumann

Katrin Schulze

Xiao Xiong

Long Huang

Nikita Xiong

Raphael Schuman

Systems and methods for converting a primary one-dimensional signal into a secondary one-dimensional signal of another modality. The primary signal is spliced into a plurality of consecutive frames. A first linear transformation transforms the frames into corresponding vectors. Positional encodings are provided on the vectors to encode relative positional information associated with each sample within each frame. A multi-head self-attention machine-learning model compares relative importance of the samples within each vector to each other in that vector to yield high-level representation vectors. A second linear transformation transforms the high-level representation vectors into corresponding secondary signal frames. The secondary signal frames are concatenated into a reconstructed one-dimensional secondary signal having a different modality than the primary signal.

Systems and methods for cross-modal signal inference using audio signals

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 voice recognition system includes a microphone configured to receive one or more spoken dialogue commands from a user in a voice recognition session. The system also includes a processor in communication with the microphone. The processor is configured to receive one or more audio files associated with one or more audio events associated with the voice recognition system, execute the one or more audio files in a voice recognition session in an audio event, and output a log report indicating a result of the audio events with the voice recognition session.

System and method for automatic testing of conversational assistance

A speaker recognition device includes a memory, and a processor. The memory stores enrolled key phrase data corresponding to utterances of a key phrase by enrolled users,and text-dependent and text-independent acoustic speaker models of the enrolled users. The processor is operatively connected to the memory, and executes instructions to authenticate a speaker as an enrolled user, which includes detecting input key phrase data corresponding to a key phrase uttered by the speaker, computing text-dependent and text-independent scores for the speaker using speech models of the enrolled user, computing a confidence score, and authenticating or rejecting the speaker as the enrolled user based on whether the confidence score indicates that the input key phrase data corresponds to the speech from the enrolled user.

Speaker identification with ultra-short speech segments for far and near field voice assistance applications

Systems and methods for detecting speech activity. The system includes an audio source and an electronic processor. The electronic processor is configured to receive a first audio signal from the audio source, buffer the first audio signal, add random noise to the buffered first audio signal, and filter the first audio stream to create a filtered signal. The electronic processor then determines a signal entropy of each frame of the filtered signal, determines an average signal entropy of a first plurality of frames of the filtered signal occurring at a beginning of the filtered signal, and compares the signal entropy of each frame of the filtered signal to the average signal entropy. Based on the comparison, the electronic processor determines a first speech endpoint located in a first frame of the filtered signal.

Detecting speech activity in real-time in audio signal

A method for spoken language understanding (SLU) includes generating a first encoded representation of words from a user based on an output of a recurrent neural network (RNN), generating an intent label corresponding to the words based on an output of a first RNN decoder based on the first encoded representation, generating a corrected plurality of words based on an output of a second RNN decoder based on the first encoded representation and the intent label, generating a second encoded representation corresponding to the plurality of corrected words using the RNN encoder based on the plurality of corrected words, and generating a machine-readable dialog phrase that includes at least one word in the plurality of corrected words assigned to at least one slot based on an output of a third RNN decoder based on the second encoded representation of the plurality of corrected words and the intent label.

Methods and systems for intent detection and slot filling in spoken dialogue systems

A system provides multi-modal user interaction. The system is configured to detect acoustic events to perform context-sensitive personalized conversations with the speaker. Conversation or communication among the speakers or devices is categorized into different classes as confidential, partially anonymous, or public. When exchange with cloud infrastructure is needed, a clear indicator is presented to the speaker via one or more modalities. Furthermore, different dialog strategies are employed in situations where conversation failures, such as misunderstanding, wrong expectation, emotional stress, or memory deficiencies, occur.

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Pongtep Angkititrakul