According to an embodiment, a pattern recognition device recognizes a pattern of an input signal by converting the input signal to a feature vector and matching the feature vector with a recognition dictionary. The recognition dictionary includes a dictionary subspace basis vector for expressing a dictionary subspace which is a subspace of a space of the feature vector, and a plurality of probability parameters for converting similarity calculated from the feature vector and the dictionary subspace into likelihood. The device includes a recognition unit configured to calculate the similarity using a quadratic polynomial of a value of an inner product of the feature vector and the dictionary subspace basis vector, and calculate the likelihood using the similarity and an exponential function of a linear sum of the probability parameters. The recognition dictionary is trained by using an expectation maximization method using a constraint condition between the probability parameters.

Disclosed are method and apparatus for speech analysis. The speech analysis apparatus and a server are capable of communicating with each other in a 5G communication environment by executing mounted artificial intelligence (AI) algorithms and/or machine learning algorithms. The speech analysis method and apparatus may collect and analyze speech data to build a database of structured speech data.

Disclosed are method and apparatus for speech analysis. The speech analysis apparatus and a server are capable of communicating with each other in a 5G communication environment by executing mounted artificial intelligence (AI) algorithms and/or machine learning algorithms. The speech analysis method and apparatus may collect and analyze speech data to build a database of structured speech data.

There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.

There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.

It discloses a serial FFT-based low-power MFCC speech feature extraction circuit, and belongs to the technical field of calculation, reckoning or counting. The circuit is oriented toward the field of intelligence, and is adapted to a hardware circuit design by optimizing an MFCC algorithm, and a serial FFT algorithm and an approximation operation on a multiplication are fully used, thereby greatly reducing a circuit area and power. The entire circuit includes a preprocessing module, a framing and windowing module, an FFT module, a Mel filtration module, and a logarithm and DCT module. The improved FFT algorithm uses a serial pipeline manner to process data, and a time of an audio frame is effectively utilized, thereby reducing a storage area and operation frequency of the circuit under the condition of meeting an output requirement.

It discloses a serial FFT-based low-power MFCC speech feature extraction circuit, and belongs to the technical field of calculation, reckoning or counting. The circuit is oriented toward the field of intelligence, and is adapted to a hardware circuit design by optimizing an MFCC algorithm, and a serial FFT algorithm and an approximation operation on a multiplication are fully used, thereby greatly reducing a circuit area and power. The entire circuit includes a preprocessing module, a framing and windowing module, an FFT module, a Mel filtration module, and a logarithm and DCT module. The improved FFT algorithm uses a serial pipeline manner to process data, and a time of an audio frame is effectively utilized, thereby reducing a storage area and operation frequency of the circuit under the condition of meeting an output requirement.

Embodiments provide an apparatus for providing a fingerprint of an input signal, wherein the apparatus is configured to determine intensity values for a plurality of time-frequency regions of the input signal, wherein the apparatus is configured to compare the intensity values associated with different time-frequency regions of the plurality of time-frequency regions, to obtain individual values of the fingerprint based on the comparison of intensity values associated with two time-frequency regions.

Embodiments provide an apparatus for providing a fingerprint of an input signal, wherein the apparatus is configured to determine intensity values for a plurality of time-frequency regions of the input signal, wherein the apparatus is configured to compare the intensity values associated with different time-frequency regions of the plurality of time-frequency regions, to obtain individual values of the fingerprint based on the comparison of intensity values associated with two time-frequency regions.

Methods for detecting fraud include receiving a plurality of call interactions; extracting a voice print of a caller from each of the call interactions; determining which call interactions are associated with a single caller by comparing and matching pairs of voice prints of the call interactions; organizing the call interactions associated with a single caller into a group; and determining that a matching phrase was spoken by the single caller in a first call interaction and second call interaction in the group.