Scansoft, Inc.

Nuance Communications, Inc.

Dragon Systems, Inc.

Allan Gold

Joel M Gould

Michael Jack Newman

Robert S Roth

Toffee A Albina

David Parmenter

Charles E Ingold

Olivier Divay

Joev Dubach

Stefan Sherwood

Daniell Stevens

Dean Sturtevant

David Abrahams

Jonathan Hood Young

Venkatesh Nagesha

Stijn Van Even

David Wilsberg Parmenter

Barton D Wright

Jonathan Watson

A method is described for user correction of speech recognition results. A speech recognition result for a given unknown speech input is displayed to a user. A user selection is received of a portion of the recognition result needing to be corrected. For each of multiple different recognition data sources, a ranked list of alternate recognition choices is determined which correspond to the selected portion. The alternate recognition choices are concatenated or interleaved together and duplicate choices removed to form a single ranked output list of alternate recognition choices, which is displayed to the user. The method may be adaptive over time to derive preferences that can then be leveraged in the ordering of one choice list or across choice lists.

Correction menu enrichment with alternate choices and generation of choice lists in multi-pass recognition systems

Recognizing punctuation in computer-implemented speech recognition includes performing speech recognition on an utterance to produce a recognition result for the utterance. A non-verbalized punctuation mark is identified in a recognition result and the recognition result is formatted based on the identification.

Automatic insertion of non-verbalized punctuation

New techniques and systems may be implemented to improve error correction in speech recognition. These new techniques and systems may be implemented to correct errors in speech recognition systems may be used in a standard desktop environment, in a mobile environment, or in any other type of environment that can receive and/or present recognized speech.

Error correction in speech recognition

Correcting incorrect text associated with recognition errors in computer-implemented speech recognition includes receiving a selection of a word from a recognized utterance. The selection indicates a bound of a portion of the recognized utterance to be corrected. A first recognition correction is produced based on a comparison between a first alternative transcript and the recognized utterance. A second recognition correction is produced based on a comparison between a second alternative transcript and the recognized utterance. The duration of the first recognition correction differs from the duration of the second recognition correction. A portion of the recognition result that is replaced with one of the first recognition correction and the second recognition correction. includes at one bound a word indicated by the selection and extends for the duration of the one of the first recognition correction and the second recognition correction with which the portion is replaced.

Error correction in speech recognition by correcting text around selected area

An action position is manipulated in computer-implemented speech recognition by receiving data representing a spoken command. The command includes a command identifier (e.g., insert before, insert after, resume with) and a designation of at least one previously-spoken word. Speech recognition is performed on the data to identify the command identifier and the designation. Finally, an action position is established relative to the previously-spoken word based on the command identifier. Text may be selected using a spoken selection command that includes a command identifier and a text block identifier identifying a block of previously-recognized text. At least one word included in the block of text is not included in the text block identifier.

Position manipulation in speech recognition

A computer enrolls a user in a speech recognition system by obtaining data representing a user's speech, the speech including multiple user utterances and generally corresponding to an enrollment text, and analyzing acoustic content of data corresponding to a user utterance. The computer determines, based on the analysis, whether the user utterance matches a portion of the enrollment text. If so, the computer uses the acoustic content of the user utterance to update acoustic models corresponding to the portion of the enrollment text. The computer may determine that the user utterance matches a portion of the enrollment text even when the user has skipped or repeated words of the enrollment text.

Non-interactive enrollment in speech recognition

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Acton, MA, United States of America

Allan Gold