Abstract: Query history expansion may be provided. Upon receiving a spoken query from a user, an adapted language model may be applied to convert the spoken query to text. The adapted language model may comprise a plurality of queries interpolated from the user's previous queries and queries associated with other users. The spoken query may be executed and the results of the spoken query may be provided to the user.

Abstract: The customization of recognition of speech utilizing context-specific language model scale factors is provided. Training audio may be received from a source in a training phase. The received training audio may be recognized utilizing acoustic and language models being combined utilizing static scale factors. A comparison may then be made of the recognition results to a transcription of the training audio. The recognition results may include one or more hypotheses for recognizing speech. Context specific scale factors may then be generated based on the comparison. The context specific scale factors may then be applied for use in the speech recognition of audio signals in an application phase.

Abstract: The customization of language modeling components for speech recognition is provided. A list of language modeling components may be made available by a computing device. A hint may then be sent to a recognition service provider for combining the multiple language modeling components from the list. The hint may be based on a number of different domains. A customized combination of the language modeling components based on the hint may then be received from the recognition service provider.

Abstract: An incremental speech recognition system. The incremental speech recognition system incrementally decodes a spoken utterance using an additional utterance decoder only when the additional utterance decoder is likely to add significant benefit to the combined result. The available utterance decoders are ordered in a series based on accuracy, performance, diversity, and other factors. A recognition management engine coordinates decoding of the spoken utterance by the series of utterance decoders, combines the decoded utterances, and determines whether additional processing is likely to significantly improve the recognition result. If so, the recognition management engine engages the next utterance decoder and the cycle continues. If the accuracy cannot be significantly improved, the result is accepted and decoding stops.

Abstract: Query history expansion may be provided. Upon receiving a spoken query from a user, an adapted language model may be applied to convert the spoken query to text. The adapted language model may comprise a plurality of queries interpolated from the user's previous queries and queries associated with other users. The spoken query may be executed and the results of the spoken query may be provided to the user.

Abstract: A semantic error rate calculation may be provided. After receiving a spoken query from a user, the spoken query may be converted to text according to a first speech recognition hypothesis. A plurality of results associated with the converted query may be received and compared to a second plurality of results associated with the converted query.

Abstract: A dynamic exponential, feature-based, language model is continually adjusted per utterance by a user, based on the user's usage history. This adjustment of the model is done incrementally per user, over a large number of users, each with a unique history. The user history can include previously recognized utterances, text queries, and other user inputs. The history data for a user is processed to derive features. These features are then added into the language model dynamically for that user.

Abstract: Architecture that employs an overall grammar as a set of context-specific grammars for recognition of an input, each responsible for a specific context, such as subtask category, geographic region, etc. The grammars together cover the entire domain. Moreover, multiple recognitions can be run in parallel against the same input, where each recognition uses one or more of the context-specific grammars. The multiple intermediate recognition results from the different recognizer-grammars are reconciled by running re-recognition using a dynamically composed grammar based on the multiple recognition results and potentially other domain knowledge, or selecting the winner using a statistical classifier operating on classification features extracted from the multiple recognition results and other domain knowledge.

Abstract: A received utterance is recognized using different language models. For example, recognition of the utterance is independently performed using a baseline language model (BLM) and using an adapted language model (ALM). A determination is made as to what results from the different language model are more likely to be accurate. Different features may be used to assist in making the determination (e.g. language model scores, recognition confidences, acoustic model scores, quality measurements, . . . ) may be used. A classifier may be trained and then used in determining whether to select the results using the BLM or to select the results using the ALM. A language model may be automatically trained or re-trained that adjusts a weight of the training data used in training the model in response to differences between the two results obtained from applying the different language models.

Abstract: A semantic error rate calculation may be provided. After receiving a spoken query from a user, the spoken query may be converted to text according to a first speech recognition hypothesis. A plurality of results associated with the converted query may be received and compared to a second plurality of results associated with the converted query.

Abstract: Query history expansion may be provided. Upon receiving a spoken query from a user, an adapted language model may be applied to convert the spoken query to text. The adapted language model may comprise a plurality of queries interpolated from the user's previous queries and queries associated with other users. The spoken query may be executed and the results of the spoken query may be provided to the user.

Abstract: A dynamic exponential, feature-based, language model is continually adjusted per utterance by a user, based on the user's usage history. This adjustment of the model is done incrementally per user, over a large number of users, each with a unique history. The user history can include previously recognized utterances, text queries, and other user inputs. The history data for a user is processed to derive features. These features are then added into the language model dynamically for that user.

Abstract: Embodiments are provided for utilizing a client-side cache for utterance processing to facilitate network based speech recognition. An utterance comprising a query is received in a client computing device. The query is sent from the client to a network server for results processing. The utterance is processed to determine a speech profile. A cache lookup is performed based on the speech profile to determine whether results data for the query is stored in the cache. If the results data is stored in the cache, then a query is sent to cancel the results processing on the network server and the cached results data is displayed on the client computing device.

Abstract: Sequential speech recognition using two unequal automatic speech recognition (ASR) systems may be provided. The system may provide two sets of vocabulary data. A determination may be made as to whether entries in one set of vocabulary data are likely to be confused with entries in the other set of vocabulary data. If confusion is likely, a decoy entry from one set of the vocabulary data may be placed in the other set of vocabulary data to ensure more efficient and accurate speech recognition processing may take place.

Abstract: Architecture that employs an overall grammar as a set of context-specific grammars for recognition of an input, each responsible for a specific context, such as subtask category, geographic region, etc. The grammars together cover the entire domain. Moreover, multiple recognitions can be run in parallel against the same input, where each recognition uses one or more of the context-specific grammars. The multiple intermediate recognition results from the different recognizer-grammars are reconciled by running re-recognition using a dynamically composed grammar based on the multiple recognition results and potentially other domain knowledge, or selecting the winner using a statistical classifier operating on classification features extracted from the multiple recognition results and other domain knowledge.

Abstract: Embodiments are provided for utilizing a client-side cache for utterance processing to facilitate network based speech recognition. An utterance comprising a query is received in a client computing device. The query is sent from the client to a network server for results processing. The utterance is processed to determine a speech profile. A cache lookup is performed based on the speech profile to determine whether results data for the query is stored in the cache. If the results data is stored in the cache, then a query is sent to cancel the results processing on the network server and the cached results data is displayed on the client computing device.

Abstract: Sequential speech recognition using two unequal automatic speech recognition (ASR) systems may be provided. The system may provide two sets of vocabulary data. A determination may be made as to whether entries in one set of vocabulary data are likely to be confused with entries in the other set of vocabulary data. If confusion is likely, a decoy entry from one set of the vocabulary data may be placed in the other set of vocabulary data to ensure more efficient and accurate speech recognition processing may take place.

Abstract: A speech recognition system uses multiple confidence thresholds to improve the quality of speech recognition results. The choice of which confidence threshold to use for a particular utterance may be based on one or more features relating to the utterance. In one particular implementation, the speech recognition system includes a speech recognition engine that provides speech recognition results and a confidence score for an input utterance. The system also includes a threshold selection component that determines, based on the received input utterance, a threshold value corresponding to the input utterance. The system further includes a threshold component that accepts the recognition results based on a comparison of the confidence score to the threshold value.