Bacchiani
Michiel Adriaan Unico Bacchiani, Summit, NJ US
Patent application number | Description | Published |
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20100094628 | System and Method for Latency Reduction for Automatic Speech Recognition Using Partial Multi-Pass Results - A system and method is provided for reducing latency for automatic speech recognition. In one embodiment, intermediate results produced by multiple search passes are used to update a display of transcribed text. | 04-15-2010 |
20110313764 | System and Method for Latency Reduction for Automatic Speech Recognition Using Partial Multi-Pass Results - A system and method is provided for reducing latency for automatic speech recognition. In one embodiment, intermediate results produced by multiple search passes are used to update a display of transcribed text. | 12-22-2011 |
Michiel A.u. Bacchiani, Summit, NJ US
Patent application number | Description | Published |
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20150127327 | CONTEXT-DEPENDENT STATE TYING USING A NEURAL NETWORK - The technology described herein can be embodied in a method that includes receiving an audio signal encoding a portion of an utterance, and providing, to a first neural network, data corresponding to the audio signal. The method also includes generating, by a processor, data representing a transcription for the utterance based on an output of the first neural network. The first neural network is trained using features of multiple context-dependent states, the context-dependent states being derived from a plurality of context-independent states provided by a second neural network. | 05-07-2015 |
20150127337 | ASYNCHRONOUS OPTIMIZATION FOR SEQUENCE TRAINING OF NEURAL NETWORKS - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters. | 05-07-2015 |
Michiel A. U. Bacchiani, Summit, NJ US
Patent application number | Description | Published |
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20100241430 | SYSTEM AND METHOD FOR USING META-DATA DEPENDENT LANGUAGE MODELING FOR AUTOMATIC SPEECH RECOGNITION - Disclosed are systems and methods for providing a spoken dialog system using meta-data to build language models to improve speech processing. Meta-data is generally defined as data outside received speech; for example, meta-data may be a customer profile having a name, address and purchase history of a caller to a spoken dialog system. The method comprises building tree clusters from meta-data and estimating a language model using the built tree clusters. The language model may be used by various modules in the spoken dialog system, such as the automatic speech recognition module and/or the dialog management module. Building the tree clusters from the meta-data may involve generating projections from the meta-data and further may comprise computing counts as a result of unigram tree clustering and then building both unigram trees and higher-order trees from the meta-data as well as computing node distances within the built trees that are used for estimating the language model. | 09-23-2010 |