International Business Machines Corporation

Orith Toledo-Ronen

Hagai Aronowitz

David Nahamoo

Noam Slonim

Jason William Pelecanos

Roy N Bar-Haim

Ron Hoory

Amir Geva

Ranit Aharonov

Shay Ben-David

Charles Arthur Jochim

Alon Halfon

Amir Menczel

A computerized text analysis method that comprises: searching a resource of information with a search query comprising at least one of: (a) the specific debatable topic, and (b) a personal derivation of the specific debatable topic, to obtain a list of indices whose index subject contains the personal derivation and/or the specific debatable topic; determining, by applying a rule-based classifier, whether the index subject of each of the indices is (i) in favor of the debatable topic or (ii) against the debatable topic; detecting, in each of the indices, hyperlinks to encyclopedic entries whose entry subjects are person names; and determining that: if the index subject of each of the one or more indices is in favor of the specific debatable topic, then the persons are in favor of the specific debatable topic, and vice versa.

Expert stance classification using computerized text analytics

A method including: generating, from a text corpus, a lexicon of unigrams and bigrams comprising an embedding for each of said unigrams and bigrams; training a machine learning classifier on a training set comprising a subset of said lexicon, wherein each of said unigrams and bigrams in said subset has a sentiment label; applying said machine learning classifier to said lexicon, to (i) predict a sentiment of each of said unigrams and bigrams, and (ii) update said lexicon with the predicted sentiments; and performing statistical analysis on said updated lexicon, to extract one or more sentiment composition lexicons, wherein each of said one or more sentiment composition lexicons is associated with a sentiment composition class.

Learning sentiment composition from sentiment lexicons

A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly, wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the at least first and second enrollment supervectors to a single enrollment generic supervector and the at least first and second verification supervectors to a single verification generic supervector; and verifying an identity of the target subject based on a fused score calculated for the verification generic supervector, wherein the fused score is calculated based on the enrollment generic supervector and the set of development generic supervectors.

Biometric authentication

Systems and methods for maintaining speaker recognition performance are provided. A method for maintaining speaker recognition performance, comprises training a plurality of models respectively corresponding to speaker recognition scores from a plurality of speakers over a plurality of sessions, and using the plurality of models to conclude whether a speaker seeking access to an environment is a non-ideal target speaker or a non-ideal non-target speaker. Using the plurality of models to conclude comprises calculating a first probability that the speaker seeking access is the non-ideal target speaker, calculating a second probability that the speaker seeking access is the non-ideal non-target speaker, and determining whether the first probability, the second probability or a sum of the first probability and the second probability is above a probability threshold.

Detection of target and non-target users using multi-session information

Method, system and computer product are provided for a computer implemented method for providing a confidence measure for speaker diarization. The method includes: receiving an audio session as unsegmented audio data; computing a spectral ratio of principal component analysis (PCA) of sections of the received audio session by a ratio between the largest eigenvalue and the second largest eigenvalue; using the PCA spectral ratio as a confidence measure for speaker diarization processing.

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Tel Aviv, Israel

Orith Toledo-Ronen