Abstract: Some embodiments relate to techniques for receiving a query comprising content; in response to the query being received, determining that the content may have at least a first semantic meaning or a second semantic meaning that is different than the first semantic meaning; and identifying a plurality of search engines to which to submit a representation of the query, the plurality of search engines comprising a first search engine identified based on the first semantic meaning and a second search engine identified based on the second semantic meaning.

Abstract: Some embodiments relate to techniques for performing a search for content, in which a user may issue a search query, and the search engine or engines to which that query is provided may be determined dynamically based on any of a variety of factors. For example, in some embodiments, the search engine or engines to which the query is provided may be determined based on the content of the search query, and/or auxiliary information such as the user's location, demographics, query history and/or browsing history.

Abstract: Some embodiments relate to techniques for receiving a query from a device, the query comprising content; determining based at least in part on the content of the query that an application is to be launched on the device; and causing the device to launch the application using at least some information determined from the content of the query. Some embodiments relate to techniques for receiving a free-form query from a user; transferring a representation of the query to at least one computer; and receiving from the at least one computer at least one instruction to launch an application on the device.

Abstract: Some embodiments relate to performing a search for content via the Internet, wherein user input specifying a search query is supplied to a mobile communications device, such as, for example, a smartphone. The mobile communications device separately issues the search query to a plurality of search engines and can receive the results from each search engine and display the results to the user. Thus, the user does not have to separately issue the query to each of the plurality of search engines.

Abstract: Some embodiments relate to a method of performing a search for content on the Internet, in which a user may speak a search query and speech recognition may be performed on the spoken query to generate a text search query to be provided to a plurality of search engines. This enables a user to speak the search query rather than having to type it, and also allows the user to provide the search query only once, rather than having to provide it separately to multiple different search engines.

Abstract: Some embodiments relate to a method of performing a search for content on the Internet, in which a user may speak a search query and speech recognition may be performed on the spoken query to generate a text search query to be provided to a plurality of search engines. This enables a user to speak the search query rather than having to type it, and also allows the user to provide the search query only once, rather than having to provide it separately to multiple different search engines.

Abstract: Some embodiments relate to a method of performing a search for content on the Internet, in which a user may issue a search query, and the search engine or engines to which that query is provided may be determined dynamically based on any of a variety of factors. For example, in some embodiments, the search engine or engines to which the query is provided may be determined based on the content of the search query, this historical access patterns of the user that issued the query, or the historical access patterns of other users.

Abstract: Embodiments of the present invention relate to searching for content on the Internet. A user may supply a search query to a device, and the device may issue the search query to a plurality of search engines, including at least one general purpose search engine and at least one site-specific search engine. In this way, the user need not separately issue search queries to each of the plurality of search engines.

Abstract: Some embodiments relate to performing a search for content via the Internet, wherein user input specifying a search query is supplied to a mobile communications device, such as, for example, a smartphone. The mobile communications device separately issues the search query to a plurality of search engines and can receive the results from each search engine and display the results to the user. Thus, the user does not have to separately issue the query to each of the plurality of search engines.

Abstract: Some embodiments relate to using a carousel to display content. In some embodiments, a carousel having a plurality of slots may be displayed in a first portion of a display of a display device, and in response to user selection of one of the plurality of slots, content that is dynamically generated based on user input may be displayed in a second portion of the display, separate from the first portion.

Abstract: Some embodiments relate to a method of performing a search for content on the Internet, in which a user may speak a search query and speech recognition may be performed on the spoken query to generate a text search query to be provided to a plurality of search engines. This enables a user to speak the search query rather than having to type it, and also allows the user to provide the search query only once, rather than having to provide it separately to multiple different search engines.

Abstract: A speech recognition system recognizes an input utterance of spoken words. The system includes a set of word models for modeling vocabulary to be recognized, each word model being associated with a word in the vocabulary, each word in the vocabulary considered as a sequence of phones including a first phone and a last phone, wherein each word model begins in the middle of the first phone of its associated word and ends in the middle of the last phone of its associated word; a set of word connecting models for modeling acoustic transitions between the middle of a word's last phone and the middle of an immediately succeeding word's first phone; and a recognition engine for processing the input utterance in relation to the set of word models and the set of word connecting models to cause recognition of the input utterance.

Abstract: A method of a continuous speech recognition system is given for discriminatively training hidden Markov for a system recognition vocabulary. An input word phrase is converted into a sequence of representative frames. A correct state sequence alignment with the sequence of representative frames is determined, the correct state sequence alignment corresponding to models of words in the input word phrase. A plurality of incorrect recognition hypotheses is determined representing words in the recognition vocabulary that do not correspond to the input word phrase, each hypothesis being a state sequence based on the word models in the acoustic model database. A correct segment of the correct word model state sequence alignment is selected for discriminative training. A frame segment of frames in the sequence of representative frames is determined that corresponds to the correct segment.

Abstract: A speech recognition system has vocabulary word models having for each word model state both a discrete probability distribution function and a continuous probability distribution function. Word models are initially aligned with an input utterance using the discrete probability distribution functions, and an initial matching performed. From well scoring word models, a ranked scoring of those models is generated using the respective continuous probability distribution functions. After each utterance, preselected continuous probability distribution function parameters are discriminatively adjusted to increase the difference in scoring between the best scoring and the next ranking models.

Abstract: A speech recognition system is disclosed which comprises a core speech recognition program and a plurality of utility program modules for adjusting various recognition parameters such as gain, sensitivity and acceptance threshold and for improving the training of word models. The system further provides a decision tree and utility controlling program module which can be invoked by a user confronting problems during the running of the core program. The system utilizes user input to traverse the decision tree and to initiate appropriate ones of the utility program modules thereby to alter the on-going behavior of the core recognition program.

Abstract: In the large vocabulary speech recognition system disclosed herein, a preliminary screening of vocabulary models is provided by applying high speed distance measuring functions. The distance measuring functions utilize subsampled or otherwise reduced representations of the unknown speech segment and the vocabulary models. The initial screening functions achieve very high speed by precalculating, for each utterance, a comparison table of distance values which can be used for all vocabulary models. The building of each comparison table is facilitated by a method which utilizes default values as initial entries and only adjusts entries which are meaningfully different from the default value.

Abstract: In the speech recognition system disclosed herein, an input utterance is submitted to both a large vocabulary isolated word speech recognition module and a small vocabulary continuous speech recognition module. The two recognition modules generate respective scores for identified large vocabulary models and for sequences of small vocabulary models. The score provided by the continuous speech recognizer is normalized on the basis of the length of the speech input utterance and an arbitration algorithm selects among the candidates identified by the recognition modules. Preferably, the competing scores from the two recognizers are scaled by a factor or factors empirically trained to minimize incursion by each of the vocabularies on correct results from the other vocabulary.

Abstract: The speech recognition system disclosed herein obtains improved recognition accuracy by employing recognition models which are discriminatively trained from a data base comprising training data from different sources, e.g., both male and female voices. A linear discriminant analysis is performed on the training data using expanded matrices in which sources are identified or labelled. The linear discriminant analysis yields respective transforms for the different sources which however map the different sources onto a common vector space in which the vocabulary models are defined.

Abstract: An input utterance is converted to a sequence of standard or prototype data frames which are compared with word models which are represented by respective sequences of standard or prototype probability states, there being a pre-calculable distance metric representing the degree of match between each prototype data frame and each prototype model state. Only distance measurements better than a calculated threshold are considered meaningful and those meaningful metrics are stored in a packed list. Also stored is an address array of offsets for locating particular meaningful metrics in the list, the address array being accessed by the corresponding frame and state indices. Also stored is an array for distinguishing meaningful and non-meaningful metrics. Accordingly, an input utterance can be evaluated by locating meaningful metrics in the packed list using the address array and by utilizing a default value for any non-meaningful metric.

Abstract: The method disclosed herein facilitates the generation of a recognition model for a non-standard word uttered by a user in the context of a large vocabulary speech recognition system in which standard vocabulary models are represented by sequences of probability distributions for various acoustic symbols. Along with the probability distributions, a corresponding plurality of converse probability functions are precalculated which represent the likelihood that a particular probability distribution would correspond to a given input acoustic symbol. For a non-standard word uttered, a corresponding sequence of acoustic symbols is generated and, for each such symbol in the sequence, the most likely probability distribution is selected using the converse probability functions.