Sri International

Minds and Technology, Inc.

Minds and Technologies, Inc.

Leonardo Neumeyer

Vassilios Digalakis

Horacio E Franco

Dimitry Rtischev

Mitchel Weintraub

Patti J Price

Jing Zheng

John W Butzberger

Diego Doval

Juan Gargiulo

Stavros Tsakalidis

Manolis Perakakis

Philip L Hubbard

Kaori Shibatani

A method of speech recognition that uses hierarchical data structures that include a top level grammar and various related subgrammars, such as word, phone, and state subgrammars. A speech signal is acquired, and a probabilistic search is performed using the speech signal as an input, and using the (unexpanded) grammars and subgrammars as possible inputs. Memory is allocated to a subgrammar when a transition to that subgrammar is made during the probabilistic search. The subgrammar may then be expanded and evaluated, and the probability of a match between the speech signal and an element of the subgrammar for which memory has been allocated may be computed. Because unexpanded grammars and subgrammars take up very little memory, this method enables systems to recognize and process a larger vocabulary that would otherwise be possible. This method also permits grammars and subgrammars to be added, deleted, or selected by a remote computer while the speech recognition system is operating, allowing speech recognition systems to have a nearly unlimited vocabulary.

Method of dynamically altering grammars in a memory efficient speech recognition system

Described herein is a system that enables service provider's to integrate speech functionality into their applications. A service provider maintains a set of application servers. To provide a particular speech service to a client of the application server, the application server causes the client to request the speech service from another set of servers. This set of servers is responsible for providing this speech service as well as others. Such speech services include recording digital speech data at the client, and storing the recordings. Later, the application servers may retrieve the recordings, and even more, retrieve data derived from the recordings, such as data generated through speech recognition processes.

Centralized processing of digital speech data originated at the network clients of a set of servers

A method for language fluency training on a computer system having an audio output device includes invoking a web browser program, receiving a pre-recorded file including a message in a spoken language from a conversation partner, playing the message to a user seeking fluency training in the spoken language from within the web browser program on the audio output device, asynchronously with playing the message, recording a user file including a message in the spoken language from the user in response to the message from within the web browser program, outputting the user file to the conversation partner and to a language instructor, receiving an instruction file including an instruction message in the spoken language from the language instructor in response to the user message and playing the instruction message to the user from within the web browser program on the audio output device.

Method and apparatus for language training

An automatic recognition system and method divides observation vectors into subvectors and determines a quantization index for the subvectors. Subvector indices can then be transmitted or otherwise stored and used to perform recognition. In a further embodiment, recognition probabilities are determined for subvectors separately and these probabilities are combined to generate probabilities for the observed vectors. An automatic system for assigning bits to subvector indices can be used to improve recognition.

Method and apparatus for automatic recognition using features encoded with product-space vector quantization

Pronunciation quality is automatically evaluated for an utterance of speech based on one or more pronunciation scores. One type of pronunciation score is based on duration of acoustic units. Examples of acoustic units include phones and syllables. Another type of pronunciation score is based on a posterior probability that a piece of input speech corresponds to a certain model such as an HMM, given the piece of input speech. Speech may be segmented into phones and syllables for evaluation with respect to the models. The utterance of speech may be an arbitrary utterance made up of a sequence of words which had not been encountered before. Pronunciation scores are converted into grades as would be assigned by human graders. Pronunciation quality may be evaluated in a client-server language instruction environment.

Method and system for automatic text-independent grading of pronunciation for language instruction

Pronunciation quality is automatically evaluated for an utterance of speech based on one or more pronunciation scores. One type of pronunciation score is based on duration of acoustic units. Examples of acoustic units include phones and syllables. Another type of pronunciation score is based on a posterior probability that a piece of input speech corresponds to a certain model, such as a hidden Markov model, given the piece of input speech. Speech may be segmented into phones and syllable for evaluation with respect to the models. The utterance of speech may be an arbitrary utterance made up of a sequence of words which had not been encountered before. Pronunciation scores are converted into grades as would be assigned by human graders. Pronunciation quality may be evaluated in a client-server language instruction environment.

Method and apparatus for automatic text-independent grading of

A method and apparatus for automatic recognition of speech adapts to a particular speaker by using adaptation data to develop a transformation through which speaker independent models are transformed into speaker adapted models. The speaker adapted models are then used for speaker recognition and achieve better recognition accuracy than non-adapted models. In a further embodiment, the transformation-based adaptation technique is combined with a known Bayesian adaptation technique.

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Palo Alto, CA, United States of America

Leonardo Neumeyer