At&t Intellectual Property I, LP

Nuance Communications, Inc.

At+t Corp.

Interactions Corporation

Cerence Operating Company

Ann K Syrdal

Alistair D Conkie

Andrej Ljolje

Yeon-Jun Kim

Mark Charles Beutnagel

Taniya Mishra

Ilija Zeljkovic

Kenneth H Rosen

Pradeep K Bansal

Carroll W Creswell

Eric Martin Zavesky

Srinivas Bangalore

Junlan Feng

Patrick Guy Haffner

Jeffrey Joseph Farah

Sumit Chopra

Juergen Schroeter

David Eugene Schulz

Ladan Golipour

Mazin E Gilbert

Mazin G Rahim

Jay Gordon Wilpon

Amanda Joy Stent

David A Kapilow

Ioannis G Stylianou

Jeffrey J Farah

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for performing speaker verification. A system configured to practice the method receives a request to verify a speaker, generates a text challenge that is unique to the request, and, in response to the request, prompts the speaker to utter the text challenge. Then the system records a dynamic image feature of the speaker as the speaker utters the text challenge, and performs speaker verification based on the dynamic image feature and the text challenge. Recording the dynamic image feature of the speaker can include recording video of the speaker while speaking the text challenge. The dynamic feature can include a movement pattern of head, lips, mouth, eyes, and/or eyebrows of the speaker. The dynamic image feature can relate to phonetic content of the speaker speaking the challenge, speech prosody, and the speaker's facial expression responding to content of the challenge.

System and method for dynamic facial features for speaker recognition

Disclosed herein are systems, computer-implemented methods, and tangible computer-readable storage media for speaker recognition personalization. The method recognizes speech received from a speaker interacting with a speech interface using a set of allocated resources, the set of allocated resources including bandwidth, processor time, memory, and storage. The method records metrics associated with the recognized speech, and after recording the metrics, modifies at least one of the allocated resources in the set of allocated resources commensurate with the recorded metrics. The method recognizes additional speech from the speaker using the modified set of allocated resources. Metrics can include a speech recognition confidence score, processing speed, dialog behavior, requests for repeats, negative responses to confirmations, and task completions. The method can further store a speaker personalization profile having information for the modified set of allocated resources and recognize speech associated with the speaker based on the speaker personalization profile.

System and method for speech personalization by need

Systems, methods, and computer-readable storage devices to improve the quality of synthetic speech generation. A system selects speech units from a speech unit database, the speech units corresponding to text to be converted to speech. The system identifies a desired prosodic curve of speech produced from the selected speech units, and also identifies an actual prosodic curve of the speech units. The selected speech units are modified such that a new prosodic curve of the modified speech units matches the desired prosodic curve. The system stores the modified speech units into the speech unit database for use in generating future speech, thereby increasing the prosodic coverage of the database with the expectation of improving the output quality.

System and method for prosodically modified unit selection databases

A system and method are disclosed for generating customized text-to-speech voices for a particular application. The method comprises generating a custom text-to-speech voice by selecting a voice for generating a custom text-to-speech voice associated with a domain, collecting text data associated with the domain from a pre-existing text data source and using the collected text data, generating an in-domain inventory of synthesis speech units by selecting speech units appropriate to the domain via a search of a pre-existing inventory of synthesis speech units, or by recording the minimal inventory for a selected level of synthesis quality. The text-to-speech custom voice for the domain is generated utilizing the in-domain inventory of synthesis speech units. Active learning techniques may also be employed to identify problem phrases wherein only a few minutes of recorded data is necessary to deliver a high quality TTS custom voice.

System and method for generating customized text-to-speech voices

A method includes receiving, at a content server from a media device, a request for media content at a first playback rate. The media content is available to the content server at a second playback rate that is different from the first playback rate. The method includes generating modified media content by modifying a first portion of the media content to have a second format corresponding to a third media playback rate. The first portion having a first media characteristic. The third playback rate is different than the first playback rate and is different than the second playback rate. The third playback rate is selected such that the modified media content has a third format corresponding to the first playback rate. The method further includes sending the modified media content from the content server to a media device.

Systems and methods of providing modified media content

Disclosed herein are systems, methods, and non-transitory computer-readable storage media relating to speaker verification. In one aspect, a system receives a first user identity from a second user, and, based on the identity, accesses voice characteristics. The system randomly generates a challenge sentence according to a rule and/or grammar, based on the voice characteristics, and prompts the second user to speak the challenge sentence. The system verifies that the second user is the first user if the spoken challenge sentence matches the voice characteristics. In an enrollment aspect, the system constructs an enrollment phrase that covers a minimum threshold of unique speech sounds based on speaker-distinctive phonemes, phoneme clusters, and prosody. Then user utters the enrollment phrase and extracts voice characteristics for the user from the uttered enrollment phrase. The system generates a user profile, based on the voice characteristics, for generating random challenge sentences according to a grammar.

System and method for generating challenge utterances for speaker verification

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for detecting and correcting abnormal stress patterns in unit-selection speech synthesis. A system practicing the method detects incorrect stress patterns in selected acoustic units representing speech to be synthesized, and corrects the incorrect stress patterns in the selected acoustic units to yield corrected stress patterns. The system can further synthesize speech based on the corrected stress patterns. In one aspect, the system also classifies the incorrect stress patterns using a machine learning algorithm such as a classification and regression tree, adaptive boosting, support vector machine, and maximum entropy. In this way a text-to-speech unit selection speech synthesizer can produce more natural sounding speech with suitable stress patterns regardless of the stress of units in a unit selection database.

System and method for automatic detection of abnormal stress patterns in unit selection synthesis

A method includes processing media content. The media content includes audio data corresponding to a first audio playback rate and video data corresponding to a first video playback rate. Processing the media content includes identifying a speech portion of the audio data. The speech portion includes a consonant portion. The method further includes producing modified media content. The modified media content is produced based on modifying the video data and modifying the audio data. Modifying the audio data includes applying a non-linear transformation to the speech portion identified in the audio data. The method further includes storing the modified media content.

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Morristown, NJ, United States of America

Ann K Syrdal