Abstract: Provided herein are methods, systems, and compositions for determining a base in a polynucleotide. In various aspects, the methods, systems, and compositions presented herein are useful for performing 4-base, 5-base, or 6-base sequencing of polynucleotide molecules, for example, from liquid biopsy samples or wherein the base is a low frequency mutation.

Abstract: A computing system for generating image data representing a speaker's face includes a detection device configured to route data representing a voice signal to one or more processors and a data processing device comprising the one or more processors configured to generate a representation of a speaker that generated the voice signal in response to receiving the voice signal. The data processing device executes a voice embedding function to generate a feature vector from the voice signal representing one or more signal features of the voice signal, maps a signal feature of the feature vector to a visual feature of the speaker by a modality transfer function specifying a relationship between the visual feature of the speaker and the signal feature of the feature vector; and generates a visual representation of at least a portion of the speaker based on the mapping, the visual representation comprising the visual feature.

Abstract: A computing system for generating image data representing a speaker's face includes a detection device configured to route data representing a voice signal to one or more processors and a data processing device comprising the one or more processors configured to generate a representation of a speaker that generated the voice signal in response to receiving the voice signal. The data processing device executes a voice embedding function to generate a feature vector from the voice signal representing one or more signal features of the voice signal, maps a signal feature of the feature vector to a visual feature of the speaker by a modality transfer function specifying a relationship between the visual feature of the speaker and the signal feature of the feature vector; and generates a visual representation of at least a portion of the speaker based on the mapping, the visual representation comprising the visual feature.

Abstract: A computing system for generating image data representing a speaker's face includes a detection device configured to route data representing a voice signal to one or more processors and a data processing device comprising the one or more processors configured to generate a representation of a speaker that generated the voice signal in response to receiving the voice signal. The data processing device executes a voice embedding function to generate a feature vector from the voice signal representing one or more signal features of the voice signal, maps a signal feature of the feature vector to a visual feature of the speaker by a modality transfer function specifying a relationship between the visual feature of the speaker and the signal feature of the feature vector; and generates a visual representation of at least a portion of the speaker based on the mapping, the visual representation comprising the visual feature.

Abstract: This document describes a data processing system for processing a speech signal for voice-based profiling. The data processing system segments the speech signal into a plurality of segments, with each segment representing a portion of the speech signal. For each segment, the data processing system generates a feature vector comprising data indicative of one or more features of the portion of the speech signal represented by that segment and determines whether the feature vector comprises data indicative of one or more features with a threshold amount of confidence. For each of a subset of the generated feature vectors, the system processes data in that feature vector to generate a prediction of a value of a profile parameter and transmits an output responsive to machine executable code that generates a visual representation of the prediction of the value of the profile parameter.

Abstract: Methods, systems and computer products as described herein are directed to a Voice Profiler. According to various embodiments, the Voice Profiler receives audio data that includes a representation of a human voice. The Voice Profiler extracts voiced segments, non-voiced segments and respiratory event segments from the input audio data. The Voice Profiler predicts a physical state of the speaker of the human voice based on respective attributes of the extracted segments. According to various embodiments, the Voice Profiler predicts the lung function of the speaker based on input audio data.

Abstract: This document describes a data processing system for processing a speech signal for voice-based profiling. The data processing system segments the speech signal into a plurality of segments, with each segment representing a portion of the speech signal. For each segment, the data processing system generates a feature vector comprising data indicative of one or more features of the portion of the speech signal represented by that segment and determines whether the feature vector comprises data indicative of one or more features with a threshold amount of confidence. For each of a subset of the generated feature vectors, the system processes data in that feature vector to generate a prediction of a value of a profile parameter and transmits an output responsive to machine executable code that generates a visual representation of the prediction of the value of the profile parameter.

Abstract: A computing system for generating image data representing a speaker's face includes a detection device configured to route data representing a voice signal to one or more processors and a data processing device comprising the one or more processors configured to generate a representation of a speaker that generated the voice signal in response to receiving the voice signal. The data processing device executes a voice embedding function to generate a feature vector from the voice signal representing one or more signal features of the voice signal, maps a signal feature of the feature vector to a visual feature of the speaker by a modality transfer function specifying a relationship between the visual feature of the speaker and the signal feature of the feature vector; and generates a visual representation of at least a portion of the speaker based on the mapping, the visual representation comprising the visual feature.

Abstract: This document describes a data processing system for processing a speech signal for voice-based profiling. The data processing system segments the speech signal into a plurality of segments, with each segment representing a portion of the speech signal. For each segment, the data processing system generates a feature vector comprising data indicative of one or more features of the portion of the speech signal represented by that segment and determines whether the feature vector comprises data indicative of one or more features with a threshold amount of confidence. For each of a subset of the generated feature vectors, the system processes data in that feature vector to generate a prediction of a value of a profile parameter and transmits an output responsive to machine executable code that generates a visual representation of the prediction of the value of the profile parameter.

Abstract: There is provided a system comprising a microphone, configured to receive an input speech from an individual, an analog-to-digital (A/D) converter to convert the input speech to digital form and generate a digitized speech, a memory storing an executable code and an age estimation database, a hardware processor executing the executable code to receive the digitized speech, identify a plurality of boundaries in the digitized speech delineating a plurality of phonemes in the digitized speech, extract a plurality of formant-based feature vectors from each phoneme in the digitized speech based on at least one of a formant position, a formant bandwidth, and a formant dispersion, compare the plurality of formant-based feature vectors with age determinant formant-based feature vectors of the age estimation database, determine the age of the individual when the comparison finds a match in the age estimation database, and communicate an age-appropriate response to the individual.

Abstract: This document describes a data processing system for processing a speech signal for voice-based profiling. The data processing system segments the speech signal into a plurality of segments, with each segment representing a portion of the speech signal. For each segment, the data processing system generates a feature vector comprising data indicative of one or more features of the portion of the speech signal represented by that segment and determines whether the feature vector comprises data indicative of one or more features with a threshold amount of confidence. For each of a subset of the generated feature vectors, the system processes data in that feature vector to generate a prediction of a value of a profile parameter and transmits an output responsive to machine executable code that generates a visual representation of the prediction of the value of the profile parameter.

Abstract: There is provided a system comprising a microphone, configured to receive an input speech from an individual, an analog-to-digital (A/D) converter to convert the input speech to digital form and generate a digitized speech, a memory storing an executable code and an age estimation database, a hardware processor executing the executable code to receive the digitized speech, identify a plurality of boundaries in the digitized speech delineating a plurality of phonemes in the digitized speech, extract a plurality of formant-based feature vectors from each phoneme in the digitized speech based on at least one of a formant position, a formant bandwidth, and a formant dispersion, compare the plurality of formant-based feature vectors with age determinant formant-based feature vectors of the age estimation database, determine the age of the individual when the comparison finds a match in the age estimation database, and communicate an age-appropriate response to the individual.

Abstract: There is provided a system for keyword recognition comprising a memory storing a keyword recognition application, a processor executing the keyword recognition application to receive a digitized speech from an analog-to-digital (A/D) converter, divide the digitized speech into a plurality of speech segments having a first speech segment, calculate a first probability of distribution of a first keyword in the first speech segment, determine that a first fraction of the first speech segment includes the first keyword, in response to comparing the first probability of distribution with a first threshold associated with the first keyword, calculate a second probability of distribution of a second keyword in the first speech segment, and determine that a second fraction of the first speech segment includes the second keyword, in response to comparing the second probability of distribution with a second threshold associated with the second keyword.

Abstract: Systems, methods, and computer program products to detect a keyword in speech, by generating, from a sequence of spectral feature vectors generated from the speech, a plurality of blocked feature vector sequences, and analyzing, by a neural network, each of the plurality of blocked feature vector sequences to detect the presence of the keyword in the speech.

Abstract: Systems, methods, and computer program products to detect a keyword in speech, by generating, from a sequence of spectral feature vectors generated from the speech, a plurality of blocked feature vector sequences, and analyzing, by a neural network, each of the plurality of blocked feature vector sequences to detect the presence of the keyword in the speech.

Abstract: Spectral components attenuated in a test denoised speech signal as a result of denoising a test speech signal are restored by representing a training undistorted speech signal as a composition of training undistorted bases, and representing a training denoised speech signal as a composition of training distorted bases. The test denoised signal decomposed as a composition of the training distorted bases. The undistorted test speech signal is then estimated as the composition of the training undistorted bases that is identical to the composition of training distorted bases.

Abstract: A method classifies data into multiple classes so that the data in each class have a class-conditional probability distribution. The class-conditional probability distributions of measured data are projected into a likelihood space. The projected class-conditional probability distributions in the likelihood space are then classified according to a discriminant classifier in likelihood space.

Abstract: A method segments an audio signal including frames into non-speech and speech segments. First, high-dimensional spectral features are extracted from the audio signal. The high-dimensional features are then projected non-linearly to low-dimensional features that are subsequently averaged using a sliding window and weighted averages. A linear discriminant is applied to the averaged low-dimensional features to determine a threshold separating the low-dimensional features. The linear discriminant can be determined from a Gaussian mixture or a polynomial applied to a bi-model histogram distribution of the low-dimensional features. Then, the threshold can be used to classify the frames into either non-speech or speech segments. Speech segments having a very short duration can be discarded, and the longer speech segments can be further extended. In batch-mode or real-time the threshold can be updated continuously.

Abstract: A microphone that transduces incident pressure waves (e.g. those representing sounds) and generates text streams as a response. The microphone is a single, integrated unit with device specifications that apply to the entire unit.

Abstract: A method extracts speech recognition features from a speech signal coded as a bitstream. First, the bitstream is decoded to recover linear predictive coding filter parameters, and a residual signal. Then, the linear predictive coding filter parameters and the residual signal are discriminatively combined into speech recognition features.