International Business Machines Corporation

Geoffrey G Zweig

George Andrei Saon

Mukund Padmanabhan

Benoit Maison

Stephane Herman Maes

David Nahamoo

Michael Alan Picheny

Ramesh Ambat Gopinath

Hong-Kwang Jeff Kuo

Chalapathy Venkata Neti

Sreeram Viswanath Balakrishnan

Stanley S Chen

Yuging Gao

Scott Elliot Axelrod

In a speech recognition system, the combination of a log-linear model with a multitude of speech features is provided to recognize unknown speech utterances. The speech recognition system models the posterior probability of linguistic units relevant to speech recognition using a log-linear model. The posterior model captures the probability of the linguistic unit given the observed speech features and the parameters of the posterior model. The posterior model may be determined using the probability of the word sequence hypotheses given a multitude of speech features. Log-linear models are used with features derived from sparse or incomplete data. The speech features that are utilized may include asynchronous, overlapping, and statistically non-independent speech features. Not all features used in training need to appear in testing/recognition.

Speech recognition utilizing multitude of speech features

Methods and arrangements for facilitating database access in speech recognition. A plurality of possible subsequences corresponding to a database entry are ascertained, a record of such subsequences and their correspondence to database entries is created, and either or both of the following are carried out: unique signatures are ascertained via determining whether a subsequence corresponding to a given database entry does not also correspond to at least one other database entry; and/or multiple occurrences of a given subsequence are found, with corresponding database entries being grouped into a confusion set.

Automatic construction of unique signatures and confusable sets for database access

Methods and arrangements using lattice-based information for unsupervised speaker adaptation. By performing adaptation against a word lattice, correct models are more likely to be used in estimating a transform. Further, a particular type of lattice proposed herein enables the use of a natural confidence measure given by the posterior occupancy probability of a state, that is, the statistics of a particular state will be updated with the current frame only if the a posteriori probability of the state at that particular time is greater than a predetermined threshold.

Lattice-based unsupervised maximum likelihood linear regression for speaker adaptation

Techniques are provided for enumerating regularly identifiable or stereotypical phrases that people commonly use to convey particular information, and where exactly in these phrases the particular information is to be found. In one embodiment, such phrases are referred to as 'regular expressions.' Using such enumerated phrases, the invention is able to automatically identify them in an input data stream and then identify and extract the particular information associated with the phrase that is being sought, e.g., important or relevant information.

Information extraction from documents with regular expression matching

A trainable radio scanner, including a station monitoring circuit to scan a plurality of radio frequencies and extract audio samples of a predetermined duration from each one of the plurality of radio frequencies having a signal strength above a reception threshold; a memory storing audio classification data and the plurality of audio samples; and an audio analyzer to analyze each one of the plurality of audio samples using the audio classification data and classifies each audio sample into a musical style category; a style discriminator to control a radio station scanning operation of the radio receiver to tune only to preferred radio stations having a radio frequency at which the corresponding audio sample is classified in at least one preferred musical style category.

Device and method for trainable radio scanning

A method of validating production of a biometric attribute allegedly associated with a user comprises the following steps. A first signal is generated representing data associated with the biometric attribute allegedly received in association with the user. A second signal is also generated representing data associated with at least one feature detected in association with the production of the biometric attribute allegedly received from the user. Then, the first signal and the second signal are compared to determine a correlation level between the biometric attribute and the production feature, wherein the validation of the production of the biometric attribute depends on the correlation level. Accordingly, the invention serves to provide substantial assurance that the biometric attribute offered by the user has been physically generated by the user.

Growing community of inventors

Greenwich, CT, United States of America

Geoffrey G Zweig