A method for detecting and categorizing topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments; clustering, by the processor, the filtered fragments into a plurality of base clusters; and clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.

Preprocessing speech signals from an indirect conduction microphone. One exemplary method preprocesses the speech signal in two stages. In stage one, an external speech sample is characterized using an auto regression model, and coefficients from the model are convolved with the internal speech signal from the indirect conduction microphone to produce a pre-conditioned internal speech signal. In stage two, a training sound is received by the indirect conduction microphone and filtered through a low-pass filter. The result is then modeled using auto regression, and inverted to produce an inverted filter model. The pre-conditioned internal speech signal is convolved with the inverted filter model to remove negative or undesirable acoustic characteristics and loss from the speech signal from the indirect conduction microphone.

Techniques for performing spoken language understanding (SLU) processing are described. An SLU component may include an audio encoder configured to perform an audio-to-text processing task and an audio-to-NLU processing task. The SLU component may also include a joint decoder configured to perform the audio-to-text processing task, the audio-to-NLU processing task and a text-to-NLU processing task. Input audio data, representing a spoken input, is processed by the audio encoder and the joint decoder to determine NLU data corresponding to the spoken input.

Methods and systems for separating an object, such as a lead, from formed tissue are provided. Specifically, a tissue slitting device is configured to engage patient formed tissue at a slitting engagement point. While the object is subjected to a first traction force, the tissue slitting device is caused to move further into the engaged tissue and slit the tissue past the point of engagement. The slitting device causes the tissue to separate along an axial direction of the length of the formed tissue and releases at least some of the force containing the object. The methods and systems are well suited for use in cardiac pacing or defibrillator lead explant procedures.

Methods and systems for separating an object, such as a lead, from formed tissue are provided. Specifically, a tissue slitting device is configured to engage patient formed tissue at a slitting engagement point. While the object is subjected to a first traction force, the tissue slitting device is caused to move further into the engaged tissue and slit the tissue past the point of engagement. The slitting device causes the tissue to separate along an axial direction of the length of the formed tissue and releases at least some of the force containing the object. The methods and systems are well suited for use in cardiac pacing or defibrillator lead explant procedures.

An electronic device having a circuitry configured to perform audio source separation on an audio input signal to obtain a separated source and configured to perform audio dubbing on the separated source based on replacement conditions to obtain a personalized separated source.

The present invention provides an audio-visual dialogue system that allows a user to create an ‘avatar’ which may be customised to look and sound a particular way. The avatar may be created to resemble, for example, a person, animal or mythical creature, and generated to have a variable voice which may be female or male. The system then employs a real-time voice conversion in order to transform any audio input, for example, spoken word, into a target voice that is selected and customised by the user. The system is arranged to facially animate the avatar using a real-time lip-synching algorithm such that the generated avatar and the target voice are synchronised.

Examples of the disclosure describe user environment aware single channel acoustic noise reduction. A noisy signal received by a computing device is transformed and feature vectors of the received noisy signal are determined. The computing device accesses classification data corresponding to a plurality of user environments. The classification data for each user environment has associated therewith a noise model. A comparison is performed between the determined feature vectors and the accessed classification data to identify a current user environment. A noise level, a speech level, and a speech presence probability from the transformed noisy signal are estimated and the noise signal is reduced based on the estimates. The resulting signal is outputted as an enhanced signal with a reduced or eliminated noise signal.

Methods and systems are provided for customizing an action. In some implementations, voice input is received from a user and a context is determined from the voice input. Potential contextual data is identified based on the context and the voice input. A level of confidence is determined for an association of the potential contextual data and the context. An action is performed based on the voice input, the potential contextual data, and the level of confidence. The potential contextual data is used to customize the action.

Techniques of conducting an online meeting involve outputting ambient sound to a participant of an online meeting. Along these lines, in an online meeting during which a participant wears headphones, the participant's computer receives microphone input that contains both speech from the participant and ambient sound that the participant may wish to hear. In response to receiving the microphone input, the participant's computer separates low-volume sounds from high-volume sounds. However, instead of suppressing this low-volume sound from the microphone input, the participant's computer renders this low-volume sound. In most cases, this low-volume sound represents ambient sound generated in the vicinity of the meeting participant. The participant's computer then mixes the low-volume sound with speech received from other conference participants to form output in such a way that the participant may distinguish this sound from the received speech. The participant's computer then provides the output to the participant's headphones.