Abstract: Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate or otherwise support one or more processes or operations for Jabba language.

Abstract: Disclosed herein are systems, computer-implemented methods, and tangible computer-readable media for handling missing speech data. The computer-implemented method includes receiving speech with a missing segment, generating a plurality of hypotheses for the missing segment, identifying a best hypothesis for the missing segment, and recognizing the received speech by inserting the identified best hypothesis for the missing segment. In another method embodiment, the final step is replaced with synthesizing the received speech by inserting the identified best hypothesis for the missing segment. In one aspect, the method further includes identifying a duration for the missing segment and generating the plurality of hypotheses of the identified duration for the missing segment. The step of identifying the best hypothesis for the missing segment can be based on speech context, a pronouncing lexicon, and/or a language model. Each hypothesis can have an identical acoustic score.

Abstract: A language processing system identifies sequential command inputs in user session data stored in logs. Each sequence command input is a first command input followed by a second command input. The system determines user actions in response to each command input. For the second command input, an action was taken at the user device in response to the command input, and there is no parsing rule associated with the action that parses to the first command input. If there is a sufficient co-occurrence of the first and second command inputs and the resulting action in the logs, then a parsing rule for the action may be augmented with a rule for the first command input.

Abstract: A speech recognition platform configured to receive an audio signal that includes speech from a user and perform automatic speech recognition (ASR) on the audio signal to identify ASR results. The platform may identify: (i) a domain of a voice command within the speech based on the ASR results and based on context information associated with the speech or the user, and (ii) an intent of the voice command. In response to identifying the intent, the platform may perform a corresponding action, such as streaming audio to the device, setting a reminder for the user, purchasing an item on behalf of the user, making a reservation for the user or launching an application for the user. The speech recognition platform, in combination with the device, may therefore facilitate efficient interactions between the user and a voice-controlled device.

Abstract: Described systems and methods allow a computer system to employ a lexicon network for word sense disambiguation (WSD). In an exemplary embodiment, each node of the lexicon network represents a gloss of a lexicon entry, while an edge represents a lexical definition relationship between two glosses. The lexicon network is ordered prior to WSD, wherein ordering the lexicon network comprises arranging the nodes of the lexicon network in an ordered sequence, and removing a set of edges to eliminate loops. In some embodiments, the position of each node within the ordered sequence is determined according to a direction and a weight of an edge connected to the respective node. The weight may represent a semantic importance of the respective edge relative to other edges of the network.

Abstract: The invention enables creation of grammar networks that can regulate, control, and define the content and scope of human-machine interaction in natural language voice user interfaces (NLVUI). More specifically, the invention concerns a phrase-based modeling of generic structures of verbal interaction and use of these models for the purpose of automating part of the design of such grammar networks.

Abstract: Provided is a process for extracting conveyance records from unstructured text documents, the process including: obtaining, with one or more processors, a plurality of documents describing, in unstructured form, one or more conveyances of interest in real property; determining, with one or more processors, for each of the documents, a respective jurisdiction; selecting, with one or more processors, from a plurality of language processing models for the English language, a respective language processing model for each of the documents based on the respective determined jurisdiction; extracting, with one or more processors, for each of the documents, a plurality of structured conveyance records from each of the plurality of documents by applying the language processing model selected for the respective document based on the jurisdiction associated with the document; and storing, with one or more processors, the extracted, structured conveyance record in memory.

Abstract: Methods, systems, and computer programs are presented for unified recognition of speech and music. One method includes an operation for starting an audio recognition mode by a computing device while receiving an audio stream. Segments of the audio stream are analyzed as the audio stream is received, where the analysis includes simultaneous checking for speech and music. Further, the method includes an operation for determining a first confidence score for speech and a second confidence score for music. As the audio stream is received, additional segments are analyzed until the end of the audio stream or until the first and second confidence scores indicate that the audio stream has been identified as speech or music. Further, results are presented on a display based on the identification of the audio stream, including text entered if the audio stream was speech or song information if the audio stream was music.

Abstract: Disclosed herein is a system, method and computer readable medium storing instructions related to semantic and syntactic information in a language understanding system. The method embodiment of the invention is a method for classifying utterances during a natural language dialog between a human and a computing device. The method comprises receiving a user utterance; generating a semantic and syntactic graph associated with the received utterance, extracting all n-grams as features from the generated semantic and syntactic graph and classifying the utterance. Classifying the utterance may be performed any number of ways such as using the extracted n-grams, a syntactic and semantic graphs or writing rules.

Abstract: Personalization for Automatic Speech Recognition (ASR) is associated with a particular device. A generalized i-vector clustering method is used to train i-vector parameters on utterances received from a device and to classify test utterances from the same device. A sub-loading matrix and a residual noise term may be used when determining the personalization. A Universal Background Model (UBM) is trained using the utterances. The UBM is applied to obtain i-vectors of training utterances received from a device and a Gaussian Mixture Model (GMM) is trained using the i-vectors. During testing, the i-vector for each utterance received from the device is estimated using the device's UBM. The utterance is then assigned to the cluster with the closest centroid in the GMM. For each utterance, the i-vector and the residual noise estimation is performed. Hyperparameter estimation is also performed. The i-vector estimation and hyperparameter estimation are performed until convergence.

Abstract: A device receives, in a technical computing environment (TCE), a first variable with a first data type and a particular number of rows, and receives a second variable with a second data type and the particular number of rows. The second data type may be different than the first data type. The device receives a command to create a table based on the first variable and the second variable, and creates, based on the command, a heterogeneous table that includes the first variable and the second variable. The device stores the heterogeneous table, and/or provides the heterogeneous table for display.

Abstract: Systems and methods are disclosed for providing automated assistance for a user by receiving a user request for assistance and determining semantics of the user request and identifying at least one domain, at least one task, and at least one parameter for the user request. The systems and methods can include searching a semantic database on the Internet for the at least one matching domain, task, and parameter and responding to the user request.

Abstract: Techniques are described herein that are capable of suggesting intent frame(s) for user request(s). For instance, the intent frame(s) may be suggested to elicit a request from a user. An intent frame is a natural language phrase (e.g., a sentence) that includes at least one carrier phrase and at least one slot. A slot in an intent frame is a placeholder that is identified as being replaceable by one or more words that identify an entity and/or an action to indicate an intent of the user. A carrier phrase in an intent frame includes one or more words that suggest a type of entity and/or action that is to be identified by the one or more words that may replace the corresponding slot. In accordance with these techniques, the intent frame(s) are suggested in response to determining that natural language functionality of a processing system is activated.

Abstract: Embodiments of the present invention disclose a method for a mobile terminal to process text, a related device, and a system. The text processing method for a mobile terminal includes: sending a request message, which carries text information and start-processing position information, to a cloud application platform, where the text information includes at least one of or any combination of text to be processed, an obtaining address of the text to be processed, and an identifier of the text to be processed; and when or after receiving a response message, which is returned by the cloud application platform, of the request message, receiving and playing an audio stream from the cloud application platform. The technical solutions provided in the present invention can satisfy a requirement of a user for “listening to” text on a mobile terminal.

Abstract: Systems and methods for spoken term detection are provided. A method for spoken term detection, comprises receiving phone level out-of-vocabulary (OOV) keyword queries, converting the phone level OOV keyword queries to words, generating a confusion network (CN) based keyword searching (KWS) index, and using the CN based KWS index for both in-vocabulary (IV) keyword queries and the OOV keyword queries.

Abstract: Methods, apparatus, and computer-readable media are described herein related to a user interface (UI) that can be implemented on a head-mountable device (HMD). The UI can include a voice-navigable UI. The voice-navigable UI can include a voice navigable menu that includes one or more menu items. The voice-navigable UI can also present a first visible menu that includes at least a portion of the voice navigable menu. In response to a first utterance comprising one of the one or more menu items, the voice-navigable UI can modify the first visible menu to display one or more commands associated with the first menu item. In response to a second utterance comprising a first command, the voice-navigable UI can invoke the first command. In some embodiments, the voice-navigable UI can display a second visible menu, where the first command can be displayed above other menu items in the second visible menu.

Abstract: A method and apparatus for correcting a named entity word in a speech input text. The method includes recognizing a speech input signal from a user, obtaining a recognition result including named entity vocabulary mark-up information, determining a named entity word recognized incorrectly in the recognition result according to the named entity vocabulary mark-up information, displaying the named entity word recognized incorrectly, and correcting the named entity word recognized incorrectly.

Abstract: A system and method for representing a textual document based on the occurrence of repeats are disclosed. The system includes a sequence generator which defines a sequence representing words forming a collection of documents. A repeat calculator identifies a set of repeats within the sequence, the set of repeats comprising subsequences of the sequence which each occur more than once. A representation generator generates a representation for at least one document in the collection of documents based on occurrence, in the document, of repeats from the set of repeats.

Abstract: Systems and methods for tuning a dictionary of a speech recognition system includes accessing a voice mail record of a user, accessing a recorded audio file of a name of the user in the voice mail record spoken by the user, providing the audio file to a speech recognition system, processing the audio file in the speech recognition system and obtaining a text result, determining whether a confidence score of the text result is below a predetermined threshold, and adding, at least, the name of the user to a list of low confidence names. Alternate spellings for the low confidence names can then be added to the dictionary.

Abstract: Various embodiments provide a system, method, and computer program product for sorting and/or selectively retrieving a plurality of documents in response to a user query. More particularly, embodiments are provided that convert each document into a corresponding document language model and convert the user query into a corresponding query language model. The language models are used to define a vector space having dimensions corresponding to terms in the documents and in the user query. The language models are mapped in the vector space. Each of the documents is then ranked, wherein the ranking is based at least in part on a position of the mapped language models in the vector space, so as to determine a relative relevance of each of the plurality of documents to the user query.

Abstract: A system and an article of manufacture for providing a prompt for real-time cognitive assistance include analyzing input from at least one environmental sensor to identify context information pertaining to a user situation, identifying a likely subsequent cognitive task of the user in the user situation based on the context information and use of a learned model, determining an action with respect to information to be suggested to the user via a corresponding prompt, wherein the determining is based on the likely subsequent cognitive task, the context information and information learned from at least to one previous user situation, computing a confidence value to represent a level of certainty in the action, and providing the prompt to the user if the action has a confidence value greater than a threshold value corresponding to the action.

Abstract: In an example embodiment, there is disclosed an apparatus comprising an audio interface configured to receive an audio signal, a data interface is configured to communicate with at least one social graph, and logic is coupled to the audio interface and the data interface. The logic is configured to identify a calling party. The logic is further configured to acquire data representative of a called party from the audio signal. The logic is configured to initiate a search of the at least one social graph for the data representative of the called party to identify the called party responsive to acquiring the data representative of the called party.

Abstract: A method provides a non-optimized list of elements, with some of the elements having multiple terms. A table of sub-elements is generated from the elements list, with each sub-element having one term only and with a number of times a sub-element appears in the elements list being weighted in the sub-elements table. A weighted singleton histogram table is generated using a singleton dictionary, and a total popularity score of each singleton is computed from the sub-elements table. For each element from the elements list, an elements score is generated based on the total popularity score of each singleton within the element. An optimally sorted list of the elements list is generated based on the elements scores.

Abstract: A method and system comprise abstracting configuration items (CI) in at least a first anomaly and a second anomaly based on type of CI. Further, CIs are matched of a common type between the first and second anomalies based on a cost function. Additionally, a similarity score is computed for the first and second anomalies based, at least in part, on the cost function of the matched CI's and based on topology of the first and second anomalies.

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for improving speech recognition accuracy using textual context. The method includes retrieving a recorded utterance, capturing text from a device display associated with the spoken dialog and viewed by one party to the recorded utterance, and identifying words in the captured text that are relevant to the recorded utterance. The method further includes adding the identified words to a dynamic language model, and recognizing the recorded utterance using the dynamic language model. The recorded utterance can be a spoken dialog. A time stamp can be assigned to each identified word. The method can include adding identified words to and/or removing identified words from the dynamic language model based on their respective time stamps. A screen scraper can capture text from the device display associated with the recorded utterance. The device display can contain customer service data.

Abstract: A computing system is operable as virtual personal assistant (VPA) to understand relationships between different instances of natural language dialog expressed by different people in a multi-person conversational dialog session. The VPA can develop a common resource, a shared intent, which represents the VPA's semantic understanding of at least a portion of the multi-person dialog experience. The VPA can store and manipulate multiple shared intents, and can alternate between different shared intents as the multi-person conversation unfolds. With the shared intents, the computing system can generate useful action items and present the action items to one or more of the participants in the dialog session.

Abstract: Disclosed herein are systems, methods, and computer-readable storage devices for building classifiers in a semi-supervised or unsupervised way. An example system implementing the method can receive a human-generated map which identifies categories of transcriptions. Then the system can receive a set of machine transcriptions. The system can process each machine transcription in the set of machine transcriptions via a set of natural language understanding classifiers, to yield a machine map, the machine map including a set of classifications and a classification score for each machine transcription in the set of machine transcriptions. Then the system can generate silver annotated data by combining the human-generated map and the machine map. The algorithm can include different branches for when the machine transcription is available, when partial results are available, when no results are found for the machine transcription, and so forth.

Abstract: This device 301 stores a first content-specific language model representing a probability that a specific word appears in a word sequence representing a first content, and a second content-specific language model representing a probability that the specific word appears in a word sequence representing a second content. Based on a first probability parameter representing a probability that a content represented by a target word sequence included in a speech recognition hypothesis generated by a speech recognition process of recognizing a word sequence corresponding to a speech, a second probability parameter representing a probability that the content represented by the target word sequence is a second content, the first content-specific language model and the second content-specific language model, the device creates a language model representing a probability that the specific word appears in a word sequence corresponding to a part corresponding to the target word sequence of the speech.

Abstract: The present disclosure relates to a syntax parsing apparatus based on syntax preprocessing and a method thereof. In specific, the present disclosure parses syntaxes that can be parsed by rules and patterns without ambiguity by syntax parsing preprocessing, draws all possible syntax parsing results by applying syntax rules based on a result of syntax parsing preprocessing in which ambiguity is partially resolved, and resolves structural ambiguity by applying a statistic syntax parsing model learned from a syntax tree attachment learning corpus so as to reduce ambiguity in rule-based syntax parsing and to resolve ambiguity by a statistics-based scheme so that parsing correctness and processing efficiency in a syntax parsing method can be enhanced.

Abstract: An image processing apparatus including: image processor which processes broadcasting signal, to display image based on processed broadcasting signal; communication unit which is connected to a server; a voice input unit which receives a user's speech; a voice processor which processes a performance of a preset corresponding operation according to a voice command corresponding to the speech; and a controller which processes the voice command corresponding to the speech through one of the voice processor and the server if the speech is input through the voice input unit. If the voice command includes a keyword relating to a call sign of a broadcasting channel, the controller controls one of the voice processor and the server to select a recommended call sign corresponding to the keyword according to a predetermined selection condition, and performs a corresponding operation under the voice command with respect to the broadcasting channel of the recommended call sign.

Abstract: A method of detecting pre-determined phrases to determine compliance quality is provided. The method includes determining whether at least one of an event or a precursor event has occurred based on a comparison between pre-determined phrases and a communication between a sender and a recipient in a communications network, and rating the recipient based on the presence of the pre-determined phrases associated with the event or the presence of the pre-determined phrases associated with the precursor event in the communication.

Abstract: Methods, systems, and techniques for keyword management are described. Some embodiments provide a keyword management system (“KMS”) configured to determine the effectiveness of multiple candidate keywords. In some embodiments, the KMS generates multiple candidate keywords based on an initial keyword. The KMS may then determine an effectiveness score for each of the candidate keywords, based on marketing information about those keywords. Next, the KMS may process the candidate keywords according to the determined effectiveness scores. In some embodiments, processing the candidate keywords includes applying rules that conditionally perform actions with respect to the candidate keywords, such as modifying advertising expenditures, modifying content, or the like.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving (i) audio data that encodes a spoken natural language query, and (ii) environmental audio data, obtaining a transcription of the spoken natural language query, determining a particular content type associated with one or more keywords in the transcription, providing at least a portion of the environmental audio data to a content recognition engine, and identifying a content item that has been output by the content recognition engine, and that matches the particular content type.

Abstract: A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.

Abstract: Embodiments are disclosed that relate to identifying phonetically similar speech grammar terms during computer program development. For example, one disclosed embodiment provides a method including providing a speech grammar development tool configured to receive input of a text representation of each of a plurality of proposed speech grammar terms, convert each text representation to a phonetic representation of the speech grammar term, compare the phonetic representation of the speech grammar term to the phonetic representations of other speech grammar terms using a weighted similarity matrix, and provide an output regarding risk of confusion between two proposed speech grammar terms based upon a comparison of the phonetic representations of the two proposed speech grammar terms. The method further includes receiving data regarding incorrect speech grammar term identification, and modifying one or more weights in the weighted similarity matrix based upon the data.

Abstract: Disclosed herein are methods, systems, and computer-readable storage media for automatic speech recognition. The method includes selecting a speaker independent model, and selecting a quantity of speaker dependent models, the quantity of speaker dependent models being based on available computing resources, the selected models including the speaker independent model and the quantity of speaker dependent models. The method also includes recognizing an utterance using each of the selected models in parallel, and selecting a dominant speech model from the selected models based on recognition accuracy using the group of selected models. The system includes a processor and modules configured to control the processor to perform the method. The computer-readable storage medium includes instructions for causing a computing device to perform the steps of the method.

Abstract: Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for recognizing speech by adapting automatic speech recognition pronunciation by acoustic model restructuring. The method identifies an acoustic model and a matching pronouncing dictionary trained on typical native speech in a target dialect. The method collects speech from a new speaker resulting in collected speech and transcribes the collected speech to generate a lattice of plausible phonemes. Then the method creates a custom speech model for representing each phoneme used in the pronouncing dictionary by a weighted sum of acoustic models for all the plausible phonemes, wherein the pronouncing dictionary does not change, but the model of the acoustic space for each phoneme in the dictionary becomes a weighted sum of the acoustic models of phonemes of the typical native speech. Finally the method includes recognizing via a processor additional speech from the target speaker using the custom speech model.

Abstract: In one embodiment, a method includes receiving one or more characters of a character string as a user enters the character string into a graphical user interface (GUI) of a computing device. The method also includes determining a degree of difficulty of the user entering the character string into the GUI of the computing device. The method further includes, if the degree of difficulty is at least approximately equal to or exceeds a pre-determined threshold, providing for display to the user an auto-suggestion for completing the character string for the user.

Abstract: A language analysis method for an electronic device storing a basic corpus and a temporary corpus is provided. The language analysis method includes steps of receiving user inputs and generating signals; converting signals into textualized information; analyzing the textualized information; obtaining a first understanding result according to the basic corpus, the vocabulary segmentation results, and the sentence segmentation results; determining whether the first understanding result is an appropriate understanding according to the context; determining one or more anaphoric vocabularies when the first understanding result is an inappropriate understanding; determining a temporary understanding result of the one or more anaphoric vocabularies and a second understanding result of the textualized information according to the context; and determining a reply for the textualized information, according to the second understanding result, the basic corpus, and the temporary corpus.

Abstract: Disclosed is a system, method, and computer program product for allowing an entity to access social media data, and to perform term analysis upon that data. The approach is capable of accessing data across multiple types of internet-based sources of social data and commentary. A user interface is provided that allows the user to view and interact with the results of performing term analysis.

Abstract: A recognition dictionary system includes a variant term accumulation unit, a variant frequency analysis unit, and a recognition dictionary update unit. The variant term accumulation unit is configured to accumulate and store a first variant term that has a meaning equivalent to a base term stored in a recognition dictionary and has first character string information that is different from character string information of the base term. The variant frequency analysis unit is configured to analyze an occurrence frequency of the first variant term accumulated and stored in the variant term accumulation unit and to determine the first variant term as a principal variant term when the occurrence frequency is higher than a first prescribed value. The recognition dictionary update unit is configured to update the recognition dictionary to register the principal variant term in the recognition dictionary as being associated with the base term.

Abstract: A speech recognition method and an electronic apparatus using the method are provided. In the method, a feature vector obtained from a speech signal is inputted to a plurality of speech recognition modules, and a plurality of string probabilities and a plurality of candidate strings are obtained from the speech recognition modules respectively. The candidate string corresponding to the largest one of the plurality of string probabilities is selected as a recognition result of the speech signal.

Abstract: A content-aware speaker recognition system includes technologies to, among other things, analyze phonetic content of a speech sample, incorporate phonetic content of the speech sample into a speaker model, and use the phonetically-aware speaker model for speaker recognition.

Abstract: A method for refining a search is provided. Embodiments may include receiving a first speech signal corresponding to a first utterance and receiving a second speech signal corresponding to a second utterance, wherein the second utterance is a refinement to the first utterance. Embodiments may also include identifying information associated with the first speech signal as first speech signal information and identifying information associated with the second speech signal as second speech signal information. Embodiments may also include determining a first quantity of search results based upon the first speech signal information and determining a second quantity of search results based upon the second speech signal information.

Abstract: A method, a computer readable medium and a system for reporting automatic speech recognition that comprises, collecting an utterance, analyzing the utterance, receiving a translation of the utterance, and determining a difference between the analyzed utterance and the translated utterance. An embodiment the disclosure includes updating the utterance analysis based upon the determined difference, correlating the analyzed utterance to the translated utterance and tracking determined difference by a translator. In another embodiment the disclose includes reporting, categorizing, sorting, and grouping the determined difference.

Abstract: Efficient empirical determination, computation, and use of an acoustic confusability measure comprises: (1) an empirically derived acoustic confusability measure, comprising a means for determining the acoustic confusability between any two textual phrases in a given language, where the measure of acoustic confusability is empirically derived from examples of the application of a specific speech recognition technology, where the procedure does not require access to the internal computational models of the speech recognition technology, and does not depend upon any particular internal structure or modeling technique, and where the procedure is based upon iterative improvement from an initial estimate; (2) techniques for efficient computation of empirically derived acoustic confusability measure, comprising means for efficient application of an acoustic confusability score, allowing practical application to very large-scale problems; and (3) a method for using acoustic confusability measures to make principled

Abstract: In some implementations, a digital work provider may provide language model information related to a plurality of different contexts, such as a plurality of different digital works. For example, the language model information may include language model difference information identifying a plurality of sequences of one or more words in a digital work that have probabilities of occurrence that differ from probabilities of occurrence in a base language model by a threshold amount. The language model difference information corresponding to a particular context may be used in conjunction with the base language model to recognize an utterance made by a user of a user device. In some examples, the recognition is performed on the user device. In other examples, the utterance and associated context information are sent over a network to a recognition computing device that performs the recognition.

Abstract: The present invention addresses the deficiencies in the prior art by providing an improved dialog for disambiguating a user utterance containing more than one intent. The invention comprises methods, computer-readable media, and systems for engaging in a dialog. The method embodiment of the invention relates to a method of disambiguating a user utterance containing at least two user intents. The method comprises establishing a confidence threshold for spoken language understanding to encourage that multiple intents are returned, determining whether a received utterance comprises a first intent and a second intent and, if the received utterance contains the first intent and the second intent, disambiguating the first intent and the second intent by presenting a disambiguation sub-dialog wherein the user is offered a choice of which intent to process first, wherein the user is first presented with the intent of the first or second intents having the lowest confidence score.

Abstract: According to certain embodiments, training a transcription system includes accessing recorded voice data of a user from one or more sources. The recorded voice data comprises voice samples. A transcript of the recorded voice data is accessed. The transcript comprises text representing one or more words of each voice sample. The transcript and the recorded voice data are provided to a transcription system to generate a voice profile for the user. The voice profile comprises information used to convert a voice sample to corresponding text.