AT&T Intellectual Property I, LP

Nuance Communications, Inc.

Murat Saraclar

Vincent Goffin

Richard William Sproat

Andrej Ljolje

Michael Dennis Riley

David Crawford Gibbon

Behzad Shahraray

Lee Begeja

Bernard S Renger

Zhu Liu

Karrie Jo Hanson

A system and method are disclosed for retrieving audio segments from a spoken document. The spoken document preferably is one having moderate word error rates such as telephone calls or teleconferences. The method comprises converting speech associated with a spoken document into a lattice representation and indexing the lattice representation of speech. These steps are performed typically off-line. Upon receiving a query from a user, the method further comprises searching the indexed lattice representation of speech and returning retrieved audio segments from the spoken document that match the user query.

System and method of lattice-based search for spoken utterance retrieval

A method and system for processing recorded communications over a network provides a communication via a network to a recording server adapted for hosting a recordable meeting. The recording server includes a processor with a memory and communicates with the network and an identification code is provided for the recordable meeting along with text related to the communication, which is stored in data storage of the recording server. At least one pointer can be inserted during or after the meeting is recorded, forming a recorded meeting with at least one pointer mapped to the text of the recorded meeting. The recorded meeting with at least one pointer is then saved into the data storage that can be accessed by an interested user.

Method for retrieving information from presentations and meetings using a network

A method and system for training an automatic speech recognition system are provided. The method includes separating training data into speaker specific segments, and for each speaker specific segment, performing the following acts: generating spectral data, selecting a first warping factor and warping the spectral data, and comparing the warped spectral data with a speech model. The method also includes iteratively performing the steps of selecting another warping factor and generating another warped spectral data, comparing the other warped spectral data with the speech model, and if the other warping factor produces a closer match to the speech model, saving the other warping factor as the best warping factor for the speaker specific segment. The system includes modules configured to control a processor in the system to perform the steps of the method.

Low latency real-time vocal tract length normalization

Systems and methods for low-latency real-time speech recognition/transcription. A discriminative feature extraction, such as a heteroscedastic discriminant analysis transform, in combination with a maximum likelihood linear transform is applied during front-end processing of a digital speech signal. The extracted features reduce the word error rate. A discriminative acoustic model is applied by generating state-level lattices using Maximum Mutual Information Estimation. Recognition networks of language models are replaced by their closure. Latency is reduced by eliminating segmentation such that a number of words/sentences can be recognized as a single utterance. Latency is further reduced by performing front-end normalization in a causal fashion.

Low latency real-time speech transcription

A method and apparatus for performing speech recognition are provided. A Vocal Tract Length Normalized acoustic model for a speaker is generated from training data. Speech recognition is performed on a first recognition input to determine a first best hypothesis. A first Vocal Tract Length Normalization factor is estimated based on the first best hypothesis. Speech recognition is performed on a second recognition input using the Vocal Tract Length Normalized acoustic model to determine an other best hypothesis. An other Vocal Tract Length Normalization factor is estimated based on the other best hypothesis and at least one previous best hypothesis.

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Jersey City, NJ, United States of America

Murat Saraclar