A system and method for providing automatic and coordinated sharing of conversational resources, e.g., functions and arguments, between network-connected servers and devices and their corresponding applications. In one aspect, a system for providing automatic and coordinated sharing of conversational resources includes a network having a first and second network device, the first and second network device each comprising a set of conversational resources, a dialog manager for managing a conversation and executing calls requesting a conversational service, and a communication stack for communicating messages over the network using conversational protocols, wherein the conversational protocols establish coordinated network communication between the dialog managers of the first and second network device to automatically share the set of conversational resources of the first and second network device, when necessary, to perform their respective requested conversational service.

System and method for providing network coordinated conversational services

Techniques for combining the results of multiple recognizers in a distributed speech recognition architecture. Speech data input to a client device is encoded and processed both locally and remotely by different recognizers configured to be proficient at different speech recognition tasks. The client/server architecture is configurable to enable network providers to specify a policy directed to a trade-off between reducing recognition latency perceived by a user and usage of network resources. The results of the local and remote speech recognition engines are combined based, at least in part, on logic stored by one or more components of the client/server architecture.

Configurable speech recognition system using multiple recognizers

Techniques for enhanced linguistic transformation are disclosed. For example, a method of linguistic transformation includes the steps of providing at least one input to a plurality of modules, wherein at least one module has a different configuration than at least another module, obtaining at least one output from each of at least a subset of the plurality of modules, and generating a set of distinct outputs. The input and the output include linguistic representations and at least a portion of the output is a result of applying one or more linguistic transformations to at least a portion of the input. Techniques for displaying a plurality of results so as to emphasize component-level differences are also disclosed. By way of example, a method of displaying a plurality of results includes the steps of determining at least one primary result within the plurality of results; displaying the at least one primary result with at least a first set of color attributes; and displaying at least one alternative result with at least a second set of color attributes. Each result represents an application of at least one linguistic transformation to a common input and each result comprises a plurality of components.

Enhanced linguistic transformation

One or more embodiments include a speech recognition learning system for improved speech recognition. The learning system may include a speech optimizing system. The optimizing system may receive a first stimulus data package including spoken utterances having at least one phoneme, and contextual information. A number of result data packages may be retrieved which include stored spoken utterances and contextual information. A determination may be made as to whether the first stimulus data package requires improvement. A second stimulus data package may be generated based on the determination. A number of speech recognition implementation rules for implementing the second stimulus data package may be received. The rules may be associated with the contextual information. A determination may be made as to whether the second stimulus data package requires further improvement. Based on the determination, one or more additional speech recognition implementation rules for improved speech recognition may be generated.

Speech recognition learning system and method

Disclosed herein are systems, methods and non-transitory computer-readable media for performing speech recognition across different applications or environments without model customization or prior knowledge of the domain of the received speech. The disclosure includes recognizing received speech with a collection of domain-specific speech recognizers, determining a speech recognition confidence for each of the speech recognition outputs, selecting speech recognition candidates based on a respective speech recognition confidence for each speech recognition output, and combining selected speech recognition candidates to generate text based on the combination.

System and method for open speech recognition

A method and system that combines voice recognition engines and resolves any differences between the results of individual voice recognition engines. A speaker independent (SI) Hidden Markov Model (HMM) engine, a speaker independent Dynamic Time Warping (DTW-SI) engine and a speaker dependent Dynamic Time Warping (DTW-SD) engine are combined. Combining and resolving the results of these engines results in a system with better recognition accuracy and lower rejection rates than using the results of only one engine.

combined engine system and method for voice recognition

A method and system that combines voice recognition engines and resolves differences between the results of individual voice recognition engines using a mapping function. Speaker independent voice recognition engines and speaker-dependent voice recognition engines are combined. Hidden Markov Model (HMM) engines and Dynamic Time Warping (DTW) engines are combined.

System and method for automatic voice recognition using mapping

A method and system that combines voice recognition engines (104, 108, 112, 114) and resolves differences between the results of individual voice recognition engines (104, 106, 108, 112, 114) using a mapping function. Speaker independent voice recognition engine (104) and speaker-dependent voice recognition engine (106) are combined. Hidden Markov Model (HMM) engines (108, 114) and Dynamic Time Warping (DTW) engines (104, 106, 112) are combined.

Yingyong Qi

Papers

combined engine system and method for voice recognition

System and method for automatic voice recognition using mapping

Combining dtw and hmm in speaker dependent and independent modes for speech recognition

System and method for voice recognition with a plurality of voice recognition engines