887 results about "Language model" patented technology

A speech understanding system includes a language model comprising a combination of an N-gram language model and a context-free grammar language model. The language model stores information related to words and semantic information to be recognized. A module is adapted to receive input from a user and capture the input for processing. The module is further adapted to receive SALT application program interfaces pertaining to recognition of the input. The module is configured to process the SALT application program interfaces and the input to ascertain semantic information pertaining to a first portion of the input and output a semantic object comprising text and semantic information for the first portion by accessing the language model, wherein performing recognition and outputting the semantic object are performed while capturing continues for subsequent portions of the input.

Certain disclosed methods and systems perform multiple different types of message recognition using a shared language model. Message recognition of a first type is performed responsive to a first type of message input (e.g., speech), to provide text data in accordance with both the shared language model and a first model specific to the first type of message recognition (e.g., an acoustic model). Message recognition of a second type is performed responsive to a second type of message input (e.g., handwriting), to provide text data in accordance with both the shared language model and a second model specific to the second type of message recognition (e.g., a model that determines basic units of handwriting conveyed by freehand input). Accuracy of both such message recognizers can be improved by user correction of misrecognition by either one of them. Numerous other methods and systems are also disclosed.

A system is disclosed for selectively applying a paraphrase alternation pattern to a textual input. The system includes a pattern identification component for processing a collection of data and identifying a plurality of potentially applicable paraphrase alternation patterns. A context model provides an objective frame of reference in which to compare one or more of the plurality of potentially applicable paraphrase alternation patterns to the textual input to determine whether the pattern can be applied without changing meaning. A language model provides a principled basis for determining the boundaries of the text segment to be modified.

Systems and methods to process and translate pinyin to Chinese characters and words are disclosed. A Chinese language model is trained by extracting unknown character strings from Chinese inputs, e.g., documents and/or user inputs/queries, determining valid words from the unknown character strings, and generating a transition matrix based on the Chinese inputs for predicting a word string given the context. A method for translating a pinyin input generally includes generating a set of Chinese character strings from the pinyin input using a Chinese dictionary including words derived from the Chinese inputs and a language model trained based on the Chinese inputs, each character string having a weight indicating the likelihood that the character string corresponds to the pinyin input. An ambiguous user input may be classified as non-pinyin or pinyin by identifying an ambiguous pinyin/non-pinyin ASCII word in the user input and analyzing the context to classify the user input.

A spelling system derives a language model for a particular domain of structured data, the language model enabling determinations of alternative spellings of queries or other strings of text from that domain. More specifically, the spelling system calculates (a) probabilities that the various query entity types—such as STREET, CITY, or STATE for queries in the geographical domain—are arranged in each of the various possible orders, and (b) probabilities that an arbitrary query references given particular ones of the entities, such as the street “El Camino Real.” Based on the calculated probabilities, the spelling system generates a language model that has associated scores (e.g., probabilities) for each of a set of probable entity name orderings, where the total number of entity name orderings is substantially less than the number of all possible orderings. The language model can be applied to determine probabilities of arbitrary queries, and thus to suggest alternative queries more likely to represent what a user intended.

Disclosed is a method and apparatus for building a domain-specific language model for use in language processing applications, e.g., speech recognition. A reference language model is generated based on a relatively small seed corpus containing linguistic units relevant to the domain. An external corpus containing a large number of linguistic units is accessed. Using the reference language model, linguistic units which have a sufficient degree of relevance to the domain are extracted from the external corpus. The reference language model is then updated based on the seed corpus and the extracted linguistic units. The process may be repeated iteratively until the language model is of satisfactory quality. The language building technique may be further enhanced by combining it with mixture modeling or class-based modeling.

A dialog device comprises a natural language interfacing device (chat interface or a telephonic device), and a natural language output device (the chat interface, a display device, or a speech synthesizer outputting to the telephonic device). A computer stores natural language dialog conducted via the interfacing device and constructs a current utterance word-by-word. Each word is chosen by applying a plurality of language models to a context comprising concatenation of the stored dialog and the current utterance thus far. Each language model outputs a distribution over the words of a vocabulary. A recurrent neural network (RNN) is applied to the distributions to generate a mixture distribution. The next word is chosen using the mixture distribution. The output device outputs the current natural language utterance after it has been constructed by the computer.

A speech recognition device includes one intention extracting language model and more in which an intention of a focused specific task is inherent, an absorbing language model in which any intention of the task is not inherent, a language score calculating section that calculates a language score indicating a linguistic similarity between each of the intention extracting language model and the absorbing language model, and the content of an utterance, and a decoder that estimates an intention in the content of an utterance based on a language score of each of the language models calculated by the language score calculating section.

Systems, methods, and apparatus for accessing distributed models in automated machine processing, including using large language models in machine translation, speech recognition and other applications.

A frequency counting unit (15A) counts occurrence frequencies (14B) in input text data (14A) for respective words or word chains contained in the input text data (14A). A context diversity calculation unit (15B) calculates, for the respective words or word chains, diversity indices (14C) each indicating the context diversity of a word or word chain. A frequency correction unit (15C) corrects the occurrence frequencies (14B) of the respective words or word chains based on the diversity indices (14C) of the respective words or word chains. An N-gram language model creation unit (15D) creates an N-gram language model (14E) based on the corrected occurrence frequencies (14D) obtained for the respective words or word chains.

A composite language model may include a composite word predictor. The composite word predictor may include a first language model and a second language model that are combined according to a directed Markov random field. The composite word predictor can predict a next word based upon a first set of contexts and a second set of contexts. The first language model may include a first word predictor that is dependent upon the first set of contexts. The second language model may include a second word predictor that is dependent upon the second set of contexts. Composite model parameters can be determined by multiple iterations of a convergent N-best list approximate Expectation-Maximization algorithm and a follow-up Expectation-Maximization algorithm applied in sequence, wherein the convergent N-best list approximate Expectation-Maximization algorithm and the follow-up Expectation-Maximization algorithm extracts the first set of contexts and the second set of contexts from a training corpus.

Speaker recognition is attempted on input speech signals concurrently with provision of input speech signals to a speech recognition system. If a speaker is recognized, a speaker dependent model which has been trained on an enrolled speaker is supplied to the speech recognition system. If not recognized, then a speaker-independent recognition model is used or, alternatively, the new speaker is enrolled. Other speaker specific information such as a special language model, grammar, vocabulary, a dictionary, a list of names, a language and speaker dependent preferences can also be provided to improve the speech recognition function or even configure or customize the speech recognition system or the response of any system such as a computer or network controlled in response thereto. A consistency check in the form of a decision tree is preferably provided to accelerate the speaker recognition process and increase the accuracy thereof. Further training of a model and/or enrollment of additional speakers may be initiated upon completion of speaker recognition and/or adaptively upon each speaker utterance.

A system and method are provided for recognizing a user's speech input. The method includes the steps for detecting the user's speech input, recognizing the user's speech input by comparing the speech input to a list of entries using language model statistics to determine the most likely entry matching the user's speech input, and detecting navigation information of a trip to a predetermined destination, where the most likely entry is determined by modifying the language model statistics taking into account the navigation information. A system and method is further provided that takes into account navigation trip information to determine the most likely entry using language model statistics for recognizing text input.

A method for creating a language model from a task-independent corpus is provided. In one embodiment, a task dependent unified language model is created. The unified language model includes a plurality of context-free grammars having non-terminals and a hybrid N-gram model having at least some of the same non-terminals embedded therein.

A language recognition system, method and program product for recognizing language based input from computer users on a network of connected computers. Each computer includes at least one user based language model trained for a corresponding user for automatic speech recognition, handwriting recognition, machine translation, gesture recognition or other similar actions that require interpretation of user activities. Network computer users are clustered into classes of similar users according to user similarities such as, nationality, profession, sex, age, etc. User characteristics are collected by sensors and from databases and, then, distributed over the network during user activities. Language models with similarities among similar users on the network are identified. The language models include a language model domain, with similar language models being clustered according to their domains. Language models identified as similar are modified in response to user production activities. After modification of one language model, other identified similar language models are compared and adapted. Also, user data, including information about user activities and language model data, is transmitted over the network to other similar users. Language models are adapted only in response to similar user activities, when these activities are recorded and transmitted over the network. Language models are given a global context based on similar users that are connected together over the network.

Method and apparatus for converting language text input into a different language text output. One embodiment may be configured to convert Pinyin into written Chinese language text. Embodiments may provide a language input mechanism that accepts phonetic language input text and generates written language output text, and that also accepts modifications to existing input text and automatically corrects the corresponding output text. Embodiments may display one or more alternative output text words and/or sentences as candidates for the transliteration of the input text. The candidate output words may be ranked according to calculated probabilities, with a most probable candidate appearing first. The user may select from the candidate output text to build an output sentence that represents the correct or preferred transliteration of the input text. A lexicon, language model and history cache of previously selected output text words may be referenced in determining the probabilities of candidate output words.

A system and method for hybrid machine translation approach is based on a statistical transfer approach using statistical and linguistic features. The system and method may be used to translate from one language into another. The system may include at least one database, a rule based translation module, a statistical translation module and a hybrid machine translation engine. The database(s) store source and target text and rule based language models and statistical language models. The rule based translation module translates source text based on the rule based language models. The statistical translation module translates source text based on the statistical language models. A hybrid machine translation engine, having a maximum entropy algorithm, is coupled to the rule based translation module and the statistical translation module and is capable of translating source text into target text based on the rule based and statistical language models.

A user may control a mobile communication facility through recognized speech provided to the mobile communication facility. Speech that is recorded by a user using a mobile communication facility resident capture facility. A speech recognition facility generates results of the recorded speech using an unstructured language model based at least in part on information relating to the recording. Determining a context of the mobile communications facility at the time speech is recorded, and based on the context, delivering the generated results to a facility for performing an action on the mobile communication facility.

In a speech recognition system which recognizes a spoken utterance consisting of a sequence of spoken words and, in response, outputs a sequence of decoded words, a method for automatically punctuating the sequence of decoded words is provided. In a vocabulary of items including words, silences, and punctuation marks, assigning at least one baseform to each punctuation mark corresponding to one of silence and a non-word noise. Additionally, the method includes the step of automatically inserting a subject punctuation mark at a given point in the sequence of decoded words when an acoustic score and a language model score associated with the subject punctuation mark produce a higher combined likelihood than the acoustic score and the language model score associated with any other item in the vocabulary for the given point in the sequence of decoded words.

A query may be applied against search engines that respectively return a set of search results relating to various items discovered in the searched data sets. However, presenting numerous and varied search results may be difficult on mobile devices with small displays and limited computational resources. Instead, search results may be associated with search domains representing various information types (e.g., contacts, public figures, places, projects, movies, music, and books) and presented by grouping search results with associated query domains, e.g., in a tabbed user interface. The query may be received through an input device associated with a particular input domain, and may be transitioned to the query domain of a particular search engine (e.g., by recognizing phonemes of a voice query using an acoustic model; matching phonemes with query terms according to a pronunciation model; and generating a recognition result according to a vocabulary of an n-gram language model.)

Disclosed embodiments provide for personalizing a voice user interface of a remote multi-user service. A voice user interface for the remote multi-user service can be provided and voice information from an identified user can be received at the multi-user service through the voice user interface. A language model specific to the identified user can be retrieved that models one or more language elements. The retrieved language model can be applied to interpret the received voice information and a response can be generated by the multi-user service in response the interpreted voice information.

A user may control a mobile communication facility through recognized speech provided to the mobile communication facility. Speech that is recorded by a user using a mobile communication facility resident capture facility is transmitted through a wireless communication facility to a speech recognition facility. The speech recognition facility generates results using an unstructured language model based at least in part on information relating to the recording. The results are transmitted to the mobile communications facility where an action is performed on the mobile communication facility based on the results and adapting the speech recognition facility based on usage.

he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.

A method adapted to multiple corpora includes training a statistical machine translation model which outputs a score for a candidate translation, in a target language, of a text string in a source language. The training includes learning a weight for each of a set of lexical coverage features that are aggregated in the statistical machine translation model. The lexical coverage features include a lexical coverage feature for each of a plurality of parallel corpora. Each of the lexical coverage features represents a relative number of words of the text string for which the respective parallel corpus contributed a biphrase to the candidate translation. The method may also include learning a weight for each of a plurality of language model features, the language model features comprising one language model feature for each of the domains.

A user may control a mobile communication facility through recognized speech provided to the mobile communication facility. Speech that is recorded by a user using a mobile communication facility resident capture facility. A speech recognition facility generates results of the recorded speech using an unstructured language model based at least in part on information relating to the recording. A function of the operating system of the mobile communication facility is controlled based on the results.

A speech recognition system provides a subword decoder and a dictionary lookup to process a spoken input. In a first stage of processing, the subword decoder decodes the speech input based on subword units or particles and identifies hypothesized subword sequences using a particle dictionary and particle language model, but independently of a word dictionary or word vocabulary. Further stages of processing involve a particle to word graph expander and a word decoder. The particle to word graph expander expands the subword representation produced by the subword decoder into a word graph of word candidates using a word dictionary. The word decoder uses the word dictionary and a word language model to determine a best sequence of word candidates from the word graph that is most likely to match the words of the spoken input.

A method of decoding an input semantic structure to generate an output semantic structure. A set of transfer mappings are provided. A score is calculated for at least one transfer mapping in the set of transfer mappings using a statistical model. At least one transfer mapping is selected based on the score and used to construct the output semantic structure.

Assigning meanings to spoken utterances in a speech recognition system. A plurality of speech rules is generated, each of the of speech rules comprising a language model and an expression associated with the language model. At one interval (e.g. upon the detection of speech in the system), a current language model is generated from each language model in the speech rules for use by a recognizer. When a sequence of words is received from the recognizer, a set of speech rules which match the sequence of words received from the recognizer is determined. Each expression associated with the language model in each of the set of speech rules is evaluated, and actions are performed in the system according to the expressions associated with each language model in the set of speech rules.

The invention involves the loading and unloading of dynamic section grammars and language models in a speech recognition system. The values of the sections of the structured document are either determined in advance from a collection of documents of the same domain, document type, and speaker; or collected incrementally from documents of the same domain, document type, and speaker; or added incrementally to an already existing set of values. Speech recognition in the context of the given field is constrained to the contents of these dynamic values. If speech recognition fails or produces a poor match within this grammar or section language model, speech recognition against a larger, more general vocabulary that is not constrained to the given section is performed.

The invention discloses a dialogue automatic reply system based on deep learning and reinforcement learning. The dialogue automatic reply system comprises a user interaction module which receives question information inputted by a user in a dialogue system interface; a session management module which records the active state of the user, wherein the active state includes historical dialogue information, user position transformation information and user emotion change information; a user analysis module which analyzes registration information and the active state of the user and portraits for the user so as to obtain user portrait information; a dialogue module which generates reply information through a language module according to the question information of the user with combination of the portrait of the user; and a model learning module which updates the language model through the reinforcement learning technology according to the reply information generated by the language model. According to the dialogue automatic reply system based on deep learning and reinforcement learning, the reply of the dialogue meeting the personality of the user can be given according to the dialogue text inputted by the user with combination of context information, the personality characteristics of the user and the intentions in the dialogue.