# Patent

## Title
System and method of recording utterances using unmanaged crowds for natural language processing

## Abstract

A system and method of recording utterances for building Named Entity Recognition (“NER”) models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of crowd-sourcing human intelligence tasking for widespread collection of utterances from a population of users. As such, obtaining large datasets for building NER models may be facilitated by the system and method disclosed herein.

## Background
RELATED APPLICATION
This application is a continuation of U.S. patent application Ser. No. 14/846,926, entitled “SYSTEM AND METHOD OF RECORDING UTTERANCES USING UNMANAGED CROWDS FOR NATURAL LANGUAGE PROCESSING,” filed Sep. 7, 2015, and which issued as U.S. Pat. No. 9,448,993 B1 on Sep. 20, 2016, the content of which is hereby incorporated by reference in its entirety.


FIELD OF THE INVENTION
The invention relates to a system and method of recording utterances for building Named Entity Recognition (“NER”) models, which are used to build dialog systems in which a computer is programmed to listen and respond to human voice dialog, using unmanaged crowds.
BACKGROUND OF THE INVENTION
Many tasks in Natural Language Processing (NLP) require large amounts of annotated or transcribed language data. It is expensive to create and transcribe this data by hand. Costs can be significantly reduced through the use of crowdsourcing to accomplish simple tasks. These cost reductions often come at the price of introducing noise and consequently lowering data quality. The challenge of crowdsourcing is therefore to efficiently filter out the noise introduced by unreliable workers in order to maintain high data quality. These and other drawbacks exist with obtaining large amounts of annotated or transcribed language data for NLP.
SUMMARY OF THE INVENTION
The invention addressing these and other drawbacks relates to a system and method of recording utterances for building NER models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of unmanaged crowds for widespread collection of utterances from a population of users. As such, obtaining large datasets for building NER models may be facilitated by the system and method disclosed herein.
To facilitate such widespread collection, a campaign administrator may design a campaign for identifying utterances to be uttered, which may be useful for building NER models. The campaign administrator may also customize campaign configuration parameters that that specify how a campaign should be conducted. Each campaign may be identified by a unique identifier.
In some instances, a campaign may be associated with different sets of utterances to be uttered. For example, a campaign may include a set of session scripts. Each session script may be identified by its own unique session script identifier and may include one or more prompts. A given prompt may be identified by its own prompt identifier and include an utterance to be uttered. Each utterance to be uttered may include one or more words, phrases, and/or other utterance to be uttered. Thus, a given session script may be associated with a set of prompts, which each specify an utterance to be uttered. During a given session, a user may be presented with the set of prompts associated with a session script. As such, a given campaign may be associated with different session scripts, each associated with its set of utterances to be uttered (which may be and typically is different than another session script's set of utterances).
The system may encode information that specifies the campaign identifier and session script identifier in a token. Thus, the campaign and utterances to be uttered are transparent to the user participating in a crowd-sourced task. In an implementation, the system may leverage crowd-sourced services to provide users with tokens.
The system may include a mobile application (“mobile app”), which programs a user's end user device, such as a mobile device, that receives the token and identifies the campaign and utterances to be uttered (e.g., through the session script identifier) based on the token. The mobile app programs the mobile device to obtain the campaign configuration parameters and utterances to be uttered based on the token.
The mobile app may configure itself based on the campaign configuration parameters. For example, to reduce instances of spamming or poor quality responses, a campaign administrator may customize an audit parameter (from among the campaign configuration parameters) to cause the mobile app to configure itself to enable audit checking. During such audit checking, the mobile app may determine whether to allow an end user device to participate in recording utterances of a user based on a number of failed audits by the end user device during a given campaign.
The mobile app may audit prompts to ensure that utterances to be uttered are actually uttered by the user. For instance, the mobile app may obtain an edit distance between text recognized from a recorded utterance and text from the utterance to be uttered. If the edit distance is greater than a threshold edit distance, then the audit will be considered to have failed. If a given end user device has failed a maximum number of audits in a given campaign, then the end user device is no longer permitted to participate in the campaign.
In some instances, the campaign administrator may wish for the utterance to be recorded under certain ambient (e.g., background) noise levels. In these instances, the campaign administrator may customize a calibration parameter (from among the campaign configuration parameters) to indicate that a calibration should be performed by the mobile app prior to allowing the user to record the user uttering the utterance. The mobile app may configure itself to perform the calibration based on the calibration parameter. For instance, prior to allowing a user to record the user uttering an utterance, the mobile app may perform a calibration procedure during which ambient noise (e.g., audio without the user uttering an utterance) is measured.
In an implementation, the campaign administrator may wish to obtain recordings in a relatively noise-free environment (e.g., one in which the ambient decibel level does not exceed a maximum level). In these instances, the calibration parameter may specify a maximum allowed ambient decibel level.
In other implementations, the campaign administrator may wish to obtain recordings in a relatively noisy environment (e.g., one in which the ambient decibel level exceeds a minimum level). In these instances, the calibration parameter may specify a minimum ambient decibel level that should be achieved before allowing a recording to occur.
The mobile app may use other campaign configuration parameters to configure itself as well. Once the mobile app has configured itself, and passed any audit checks, calibration checks, etc., the mobile app may display an utterance to be uttered and record the utterance. The mobile app may continue this process until all utterances associated with a given session script identifier has been recorded. A given recording may be uploaded, either immediately after the recording is made, or in a batch process in which multiple recordings are stored and then uploaded, for analysis. In some instances, the users may be compensated for participation in recording the utterances. In these instances, the mobile app may generate and display a completion code, which may be used to validate the session during which the utterance was recorded and compensate the user.
These and other objects, features, and characteristics of the system and/or method disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates a system for recording utterances to be spoken for building NER models, according to an implementation of the invention.
FIG. 2 illustrates an end user device for recording utterances to be spoken for building NER models, according to an implementation of the invention.
FIG. 3 illustrates a flow diagram of a process of recording utterances to be spoken for building NER models, according to an implementation of the invention.
FIG. 4 illustrates a flow diagram of a process of configuring an application at an end user device for recording utterances to be spoken for building NER models, according to an implementation of the invention.
FIG. 5 illustrates a flow diagram of a process of recording utterances for building NER models, according to an implementation of the invention.
FIG. 6 illustrates a screenshot of a user interface for recording utterances, according to an implementation of the invention.



DETAILED DESCRIPTION OF THE INVENTION
The invention addressing these and other drawbacks relates to a system and method of recording utterances for building NER models, which are used to build dialog systems in which a computer listens and responds to human voice dialog.
Exemplary System Architecture
FIG. 1 illustrates a system 100 for recording utterances to be spoken for building NER models, according to an implementation of the invention. In an implementation, system 100 may include a cloud services provider 101, a transcription system 102, a crowd sourcing service 105, one or more end user devices 110, and/or other components. Each component of system 100 may be communicably coupled to one another by one or more computer networks 107.
Transcription system 102 may configure and provide campaigns customized by a campaign manager. An individual campaign may include a single instance of a data collection effort. A campaign may specify a set of utterances to be spoken by users and a set of campaign parameters. In some instances, a given campaign may include different sets of utterances to be uttered by users during different sessions. For example, a given campaign may include a first set of utterances to be uttered by a user during a first session and a second set of utterances to be uttered by the user or other user during a second session different from the first session. In these instances, each set of utterances may be assigned with a session identifier. Thus, a campaign identifier, which identifies a campaign, may be associated with a set of session identifiers, each session identifier identifying a set of utterances to be uttered. In this manner, the system may be used to collect utterances related to words, sounds, or other utterances uttered by users for building NER models. A given campaign manager may use system 100 to configure multiple campaigns, each with its own set of utterances to be uttered by users.
An example of a system that performs the functions of transcription system 102 is described further in co-pending U.S. patent application Ser. No. 14/846,925, filed on Sep. 7, 2015, entitled “SYSTEM AND METHOD FOR PROVIDING WORDS OR PHRASES TO BE UTTERED BY MEMBERS OF A CROWD AND PROCESSING THE UTTERANCES IN CROWD-SOURCED CAMPAIGNS TO FACILITATE SPEECH ANALYSIS,” now U.S. Pat. No. 9,361,887, which is incorporated by reference herein in its entirety.
In some instances, some or all campaign information (e.g., campaign parameters, session identifiers, utterances to be spoken, and/or other information that specify the campaign) may be stored using online storage system 103, which may be provided by a cloud services provider 101. Online storage system 103 may include the Amazon S3TM (referred to hereinafter as “S3” in various examples), Microsoft AzureTM, Google DriveTM, and/or other storage solutions available through a network such as the Internet. Cloud services provider 101 may provide other services, such as computer processing services, as well. In some instances, some or all of the information related to the campaigns may be stored in other storage systems to which transcription system 102 has access.
Wherever the information related to the campaigns is stored, campaigns may be distributed to users via crowd sourcing services 105. Such services may include, for example, Amazon Mechanical TurkTM, CrowdflowerTM, and/or other service that facilitates human intelligence tasks from users who participate in completing predefined tasks, typically, though not necessarily, in exchange for compensation. Campaigns may be distributed to user via alternative or additional channels (e.g. directly from transcription system 102, online marketing, etc.) as well.
Whichever channel is used to distribute a given campaign, transcription system 102 may generate information that specifies the campaign identifier and/or session identifier. The information may be in the form of a token. In a particular implementation, when a campaign is associated with a set of sessions, the token may be configured to identify the campaign and a particular session identifier that identifies a particular set of utterances to be uttered. In this manner, a given token may identify a given campaign and a particular one of its corresponding set of utterances. As such, a token may be used to obtain utterances to be uttered by a user, as well as campaign configurations to be used to conduct collection of the utterances uttered by the user.
Transcription system 102 may cause the information that specifies the campaign identifier and/or session identifier to be provided to users. For example, transcription system 102 may cause a given token to be provided to a user through crowd sourcing services 105, which provides the token to the user via a website or other user interface that displays the token (e.g., displays an alpha-numeric string that is typed into end user device 110 by the user or displays a machine readable code that is imaged and read by the end user device, etc.).
End user device 110 may be programmed to obtain the information based on a campaign identifier. For example, end user device 110 may receive a token and obtain the campaign parameters and utterances to be spoken based on the received token. End user device 110 may then collect the utterances and generate an audio file or other representation of the utterances uttered by the user. Upon completion, end user device 110 may generate a completion code, which is provided to transcription system 102 either directly and/or through crowd sourcing services 105. The completion code may be used to validate the session and compensate the user, flag the utterances associated with the completion code as having been completed by the user, and/or for other purposes as described herein.
Having described a high level overview of the system, attention will now be turned to a more detailed description of end user device 110.
Configuring the Collection Application and Obtaining Utterances
FIG. 2 illustrates an end user device 110 for recording utterances to be spoken for building NER models, according to an implementation of the invention. End user device 110 may be configured as a smartphone, a desktop computer, a laptop computer, and/or other device that can be programmed to collect user utterances as described herein.
End user device 110 may include one or more processors 212 (also interchangeably referred to herein as processors 212, processor(s) 212, or processor 212 for convenience), one or more storage devices 214 (which may store various instructions described herein), and/or other components. Processors 212 may be programmed by one or more computer program instructions. For example, processors 212 may be programmed by an utterance collection application 220 and/or other instructions 230 that program end user device 110 to perform various operations. As used herein, for convenience, the various instructions will be described as performing an operation, when, in fact, the various instructions program the processors 212 (and therefore end user device 110) to perform the operation. Utterance collection application 220 may be downloaded from a third party application distributor, such as the Google PlayTM store, Apple iTunesTM store, Blackberry WorldTM, and/or other third party application distributors. Alternatively or additionally, utterance collection application 220 may be downloaded directly from transcription system 102 or end user device 110 may be pre-programmed with utterance collection application 220.
FIG. 3 illustrates a flow diagram of a process 300 of recording utterances to be spoken for building NER models, according to an implementation of the invention. Process 300 may be implemented by utterance collection application 220 (which programs end user device 110 to perform process 300) or other system component programmed to implement process 300.
Initializing and Configuring the Utterance Collection Application
In an implementation, in an operation 302, process 300 may include receiving a token from a user. For instance, upon application startup at end user device 110, utterance collection application 220 may cause the end user device 110 to display a user interface that includes an input member configured to receive a token. The user may input the token via the input member. The token may be in the form of an alpha-numeric string, a machine readable code (e.g., a QR code, barcode, etc.), a number, and/or and other information that conveys a campaign identifier and/or a session identifier.
In an implementation, in an operation 304, process 300 may include obtaining campaign configuration parameters based on the token. For instance, the token may include a campaign identifier. In a particular implementation, the token may be configured as a string such as “ABCD12345”, in which the first N (e.g., four) characters of the string encode the campaign identifier (e.g., “ABCD” in the foregoing example). In other instances, the token may be used to lookup, via a database search, the campaign identifier. Other methods of encoding a campaign identifier may be used as well. Regardless of the manner in which the token encodes the campaign identifier, process 300 may obtain the campaign identifier based on the token and obtain the campaign configuration parameters based on the campaign identifier.
In a particular implementation, the campaign configuration parameters may be stored at online storage system 103 (or other storage system) at a particular file location. In the file location, the campaign configuration parameters may be stored as filenames that begin with the campaign code. For example, the campaign configuration parameters may be stored in a configuration file whose prefix includes the campaign code and file extension includes “.config.” The configuration file may written using JavaScript Object Notation, eXtensible Markup Language, or other data interchange format.
In a particular example, if a given campaign code is “ABCD,” the corresponding configuration file will be named “ABCD.config.” Thus, the token may encode the campaign code, which is then used to identify a corresponding configuration file that includes the campaign configuration parameters. Each time utterances for a given campaign should be collected, a token encoding the corresponding campaign code may be issued to one or more users (through crowd sourcing services 105 or otherwise).
Other ways of obtaining the campaign configuration parameters may be used as well. For instance, the campaign configuration parameters may be stored in a relational database in association with the campaign code (and/or a token identifier). The relational database may be queried to obtain the campaign configuration parameters. Other types of databases and schemes for associating the campaign code with its campaign configuration parameters may be used as well.
In an implementation, in an operation 306, process 300 may include configuring the utterance collection application based on the campaign configuration parameters. For instance, utterance collection application 220 may configure itself based on the campaign configuration parameters, as described in more detail with respect to FIG. 4.
In an implementation, in an operation 308, process 300 may include obtaining the utterances to be uttered by the user based on the token. In an implementation, the token may include a session script identifier that identifies a session script, which includes one or more prompts.
Each prompt may include a single utterance (e.g., set of words, phrases, or other utterance) to be uttered. For example, a given prompt may include one or more words or phrases, and/or other utterances to be uttered. For instance, the given prompt may include the words “POI,” “Seattle,” “Washington,” and “USA.” Other utterances to be spoken may be used as well.
In a particular implementation, the token may be configured as a string, in which a subset (e.g., five) of the characters of the string encode the session script identifier. Continuing the foregoing example in which token “ABCD12345” is used, the session script identifier may be identified based on the next N (e.g., 5) characters after the campaign code—in this case, the session script identifier will be “12345”. In other instances, the token may be used to lookup, via a database search, the session script identifier. Other methods of encoding a session script identifier may be used as well. Regardless of the manner in which the token encodes the session script identifier, process 300 may obtain the session script identifier based on the token and obtain the utterances to be uttered based on the session script identifier.
In a particular implementation, a session script may be stored at online storage system 103 (or other storage system) at a particular file location. In the file location, the session script may be stored as a filename that begin with the session script identifier. For example, the session script may be stored in a session script file whose prefix includes the session script identifier and file extension includes “.ss”. The session script file may written using JavaScript Object Notation, eXtensible Markup Language, or other data interchange format.
In a particular example, if a given session script identifier is “12345” the corresponding session script file will be named “12345.ss”. Thus, the token may encode the session script code, which is then used to identify a corresponding session script file that includes one or more prompts each having an utterance to be uttered.
Other ways of obtaining the session script may be used as well, such as using the aforementioned relational database or other techniques described with respect to obtaining the campaign configuration parameters.
In an implementation, in an operation 310, process 300 may include conducting the collection of utterances, which may include providing a user interface for recording the utterances uttered by the user. The collection/recording of utterances is described in more detail with respect to FIGS. 5 and 6.
In an implementation, in an operation 312, process 300 may include saving and/or uploading the recorded utterances. In some implementations, the recorded utterances may be stored at end user device 110 and later uploaded (e.g., after a session is complete, as a batch process in which uploads occur at intervals, etc.). In these implementations, the recorded utterances may be stored locally at end user device 110 using the session script as a file prefix. Later, if the recordings are to be uploaded, the remote storage at which the recordings are uploaded is checked to determine whether a given recording has been previously uploaded (based on the session script identifier) and if not, the recording is uploaded. In some instances, the campaign administrator may obtain, through system 100, the recording directly from the end user device 110.
In other implementations, the recorded utterances may be uploaded as soon as they are recorded. For instance, when the user engages the “next” interface member to indicate the current prompt is complete and to obtain the next prompt, the recorded utterance from the current prompt may be uploaded. In these implementations, storage requirements imposed on the end user device 110 may be reduced.
In any instance where the recordings are uploaded, the uploads may be stored at a particular file location for the uploads. For instance, recordings for a given session (in which prompts for a session script are uttered by the user and recorded by end user device 110) may be stored in the upload file location under a sub-directory named based on a device identifier (e.g., a Media Access Control identifier and/or other identifier that uniquely identifies an end user device). Each recording may be named according to the session script identifier and a timestamp associated with the recording.
In an implementation, in an operation 314, process 300 may generating and providing a completion code if the campaign configuration parameters indicate that a completion code should be provided to the user. The completion code may be in the form of a string or other encoding that may be validated to compensate the user for participating in the task of uttering a specified utterance. For example, utterance collection application 220 may provide the completion code to the user via a display of an end user device and/or through a communication channel such as electronic mail, Short Messaging Service (“SMS”) message, or other communication channel. In some instances, utterance collection application 220 may provide the completion code via communication channel to transcription system 102. In some instances, utterance collection application 220 may upload the completion code to the file area (or database) at which campaigns, session scripts, etc., are stored.
FIG. 4 illustrates a flow diagram of a process 400 of configuring an application at an end user device for recording utterances to be spoken for building NER models, according to an implementation of the invention.
In an implementation, in an operation 402, process 400 may include determining whether the campaign configuration parameters specify that an audit check should be performed. If so, in an operation, 404, process 400 may include determining whether to disqualify the end user device based on whether a number of failed audits associated with the end user device exceeds a threshold number of allowed failures (which may be specified by the campaign configuration parameters). The number of failed audits may be based on the number of audits that a given end user device 110 failed during a given campaign.
Auditing an End User Device
An end user device 110 may fail an audit when a user of end user device 110 incorrectly uttered an utterance associated with a prompt. Since a given prompt is associated with a given session script, which is in turn associated with a campaign, an end user device 110 is, in some instances, permitted only a limited number of failed audits for a given campaign. This is to ensure that the recorded utterances achieve a certain level of quality, and minimize the number of spammers or otherwise low quality users who provide utterances (through their end user devices).
To audit an end user device, utterance collection application 220 may perform Automated Speech Recognition (“ASR”) to determine the text of the utterance uttered by a user or otherwise obtain the text of the utterance uttered by the user through ASR. In some instances, prompt within a session script may be predefined as being an audit utterance. In other words, at least some utterances may be tagged as being for audit purposes, and may or may not be part of the overall campaign of utterances for which the campaign administrator wishes to obtain recordings.
Whether or not an utterance is a dedicated audit utterance, utterance collection application 220 may obtain an edit distance between the utterance uttered by the user and the words, phrases, or other utterance specified by a given prompt. Such edit distance may be supplied by third parties or otherwise use conventional edit distance algorithms that provide a measure of different between two strings. If the edit distance is greater than a threshold edit distance, the audit is considered to have been failed. If so, utterance collection application 220 may generate and store an indication of the failure. In some instances, utterance collection application 220 may store audit information and save the audit information, locally and/or via file area. The audit information may include an indication of the audit result (e.g., failed or passed), the campaign identifier associated with the audit result, the session script identifier associated with the audit result, the prompt identifier associated with the audit result, the recorded utterance, the edit distance, and/or other information associated with the audit. As such, a record of the audit may be maintained and accessible.
In some instances, if the audit failed, an array or other data structure may be stored such that the number of failed audits for a given campaign may be determined. The number of failed audits may, alternatively or additionally, be determined based on the stored audit information.
In an implementation, in an operation 406, process 400 may include determining whether the number of sessions have been exceeded. For instance, the end user device may be permitted a maximum number of sessions in which to participate in a given campaign and/or the campaign may be associated with a total number of sessions that the campaign administrator wishes to collect utterances. If the total number of sessions has been exceeded, then the session is discontinued, and the user is informed that the maximum number of sessions has been reached in an operation 408.
In an implementation, in an operation 410, process 400 may determine whether the campaign configuration parameters specify that a tutorial should be presented to the user. If yes, then in an operation 412, the tutorial may be provided, as described herein.
In an implementation, in an operation 414, process 400 may include determining whether the campaign configuration parameters specify that demographics of the user should be collected. If yes, then demographic information of the user uttering the utterances may be collected in an operation 416. For instance, utterance collection application 220 may provide an interface that includes various inputs configured to receive demographic information from the user. The campaign configuration parameters may specify which demographic information to seek from the user so that the campaign administrator is able to obtain particular demographic information from the users who participate.
In an implementation, in an operation 418, process 400 may include determining whether the campaign configuration parameters specify that calibration should be performed. If yes, in an operation 420, calibration prior to recording utterances may be caused to be conducted. For instance, utterance collection application 220 may store a flag or other data indicator to indicate that calibration should be performed prior to allowing the user to record the utterances.
In an implementation, in an operation 422, process 400 may include obtaining other campaign configuration parameters (described herein) and configuring the utterance collection application 220 accordingly.
FIG. 5 illustrates a flow diagram of a process 500 of conducing the collection of utterances for building NER models, according to an implementation of the invention.
In an implementation, in an operation 502, process 500 may include determine whether the campaign configuration parameters specify that calibration should be performed.
If yes, in an implementation, in an operation 504, process 500 may include testing audio to conduct calibration. For example, utterance collection application 220 may sample live audio or record a predefined time period of audio in order to determine whether ambient noise (e.g., audio without a user uttering an utterance) passes calibration.
In an implementation, in an operation 506, process 500 may include determining whether the tested audio passes the calibration check. In an implementation, depending on a calibration parameter (among the campaign configuration parameters), a calibration check may require that a maximum level of ambient noise (e.g., a maximum decibel level) not be exceeded. For instance, the calibration check may ensure that the ambient noise is sufficiently low (e.g., below a predefined threshold decibel limit) so that the utterances can be properly recorded with minimal background noise. In other implementations, the calibration check may require that a minimum level of ambient noise (e.g., a minimum decibel level) be present. In these implementations, a campaign administrator may wish to obtain utterances in noisy areas (such as in bus stations, airports, etc.) in order to determine how utterances sound when background noise is present (e.g., above a certain decibel level).
Whichever type of calibration is specified, if the calibration check fails (e.g., the audio does not satisfy the calibration parameter), then utterance collection application 220 may request that the user try again (and in some instances provide a hint to move to a quieter or louder location) and processing may return to operation 504.
In an implementation, if the calibration check is passed or if no calibration check is specified, in an operation 508, process 500 may include obtaining and displaying a first (or next) prompt that includes an utterance to be uttered.
In an implementation, in an operation 510, process 500 may include recording the utterance and receiving an indication that the user completed the utterance. For instance, utterance collection application 220 may receive an indication that the “next” user interface member has been selected.
In an implementation, in an operation 512, process 500 may include determining whether any more prompts for the session is available. If yes, then processing may return to operation 508, where the next prompt is obtained and displayed. If no, then processing may proceed to operation 312 (illustrated in FIG. 3).
FIG. 6 illustrates a screenshot of a user interface 600 for recording utterances, according to an implementation of the invention. User interface member 602 may include a display area that displays an utterance to be uttered by the user. As illustrated, the utterance “play instant message” is to be uttered by the user. User interface member 604 may display a number of utterances (including any audit utterances) performed (“1”) and remaining (“24”). User interface members 606 may include controls for progressing through recordings. For instance, user interface member 606A may include a button or other type of user input that, when selected by the user, specifies that the current utterance should be redone. For instance, user interface member 606B may include a button or other type of user input that, when selected by the user, specifies recording of the current utterance should commence. User interface member 606C may include a button or other type of user input that, when selected by the user, specifies that the current utterance is complete and that the next utterance (if any) should be displayed for the user to utter and record. In some instances, selection of the next button may also cause the recording of the current recording to be uploaded (if a network connection is not available, then the current recording will be stored locally until a network connection is available).
Examples of Configurations and Files Locations
The following configuration and other information provides an exemplary way to store and define various settings described herein. As would be appreciated, other ways of storing and conveying the following may be used as well.
Campaign Configuration Parameters
S3 Location: data/settings/
After the user enters the token and utterance collection application 220 extracts the campaign code from it, utterance collection application 220 may use it to check whether a .config file exists within data/settings. If one does, it may download and parse the .config file.
The file contains necessary information to configure the application, such as decibel levels, a number of sessions a user is permitted to participate, and which file locations to find tutorial and demographic information.
Table 1 describes various campaign configuration parameters. The following table is provided for illustrative purposes and should not be construed as limiting, as other campaign configuration parameters may be used as well.













Key
Description
Type
Example


















version
Schema version of the
Double
1.0



settings file.




id
Same as the filename. Used
String
“ao9hz3”



to easily identify the





settings file.




db_levels
Defines the different decibels





levels to be used by the VU





meter during prompt recording





and calibration.




db_levels.min
The minimum decibel level.
Double
0


db_levels.grn_
The maximum decibel level
Double
10.5


max_ylw_min
for the green range and the





minimum for the yellow.




db_levels.ylw_
The maximum decibel level
Double
20


max_red_min
for the yellow range and the





minimum for the red.




db_levels.max
The maximum decibel level.
Double
30


tutorial
Name of the .tut file to
String
“test_tut”



download tutorials from S3.





Exclude to skip tutorials.





Should not contain spaces.




demographic
Name of the .dem file to
String
“test_dem”



download demographic





options from S3. Exclude to





skip demographic. Should





not contain spaces.




session_
Name of folder within
String
“test_campaign”


script_dir
data/sessions.scripts in





which campaign's session





scripts are kept.




number_of_
The number of sessions [1 −
Integer
1


sessions
n] a user can do.




duplicate_
Whether a device can repeat
Boolean
true|false


sessions
a session. Should be true if a





device is shared.




do_calibration
Whether the calibration
Boolean
true|false



screen should be displayed.




external_
Whether temporary,
Boolean
true|false


storage
completed, and zip files





should be saved in the app's





internals storage (more





secure) or external folder. If





external, an app folder may





be created.




failed_audits_
Total number of failed
Integer
10


threshold
audits a given device is





allowed. If a device's total





equals this value, device





may not be able to continue





with campaign.




upload
Whether to automatically
Boolean
true|false



upload audio files and other





information.




generate_
Whether to generate an 8-
Boolean
true|false


completion_
digit alphanumerical string




code
at the end of each completed





session




max_audio_ 
The maximum length an
Long
12000


length
audio recording can be, in





milliseconds.




silence
Silence to precede and





following each audio





recording




silence.leading
Duration of silence
Long
1000



prepending the audio





recording, in milliseconds.





Default is 500




silence.trailing
Duration of silence to
Long
5000



append the audio recording,





in milliseconds. Default is





1000









Tutorial
Filename: <tutorial_value_from_config>.tut
S3 Location: data/tutorials/
A tutorial may help a new user familiarize themselves with utterance collection application 220, its features, and how to properly and successfully record and complete a session.
The contents of a tutorial may be derived from a .tut file found within the data/tutorials bucket containing the JSON fields described below.
The name of the tutorial file should be the same as the value of the tutorial field within the .config file. For example, if the .config file's tutorial field's value was test_tutorial, then the tutorial file should be named test_tutorial.tut.
Table 2 describes the content of a tutorial (“.tut”) file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.













Key
Description
Type
Example







version
Schema version of
Double
1.0



the tutorial file.




slides
Each node within the array





contains information for





one page of the tutorial.




slides.title
The title of the tutorial slide.
String
“Step 1”


slides.body
Text to be displayed as
String
“Lorem ipsum



part of the tutorial. New

dolor. . . \



paragraphs must be

nDonec et



denoted with newline

fermentum 



escape character (\n).

dui . . . ”


slides.image
Information about the
JSON




image to be displayed on
Object




the tutorial page.




slides.image.url
URL from where the
String
“www.site.com/



image can be

image1.jpg”



downloaded by app.




slides.video
Information about the
JSON




video to be displayed on
Object




the tutorial page.




slides.video.url
The YouTube video's ID
String
“BY651oCxGiQ”









Demographic Information
Filename: <demographic_value_from_config>.dem
S3 Location: data/demographics/
A campaign administrator may choose to collect demographic information about the user. In order to do so, they must create a .dem file containing the following fields and upload it to the data/demographic bucket on S3. Furthermore, in the .config file defining the campaign, the must provide a demographic field with the value set to the name of the .dem file.
The name of the demographic file should be the same as the value of the demographic field within the .config file. For example, if the .config file's demographic field's value was test_demographic, then the demographic file should be named test_demographic.dem.
After a user has filled out the demographic information, utterance collection application 220 may create a .dem.info file with the filename being the same as the device's UUID and upload it to the upload directory as described within the In-Session Uploading section.
Table 3 describes the content of a demographic (“.dem”) file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.













Key
Description
Type
Example







version
Schema version of the
Double
1.0



demographic file.




data_points





data_points.label
Name of the field. May 
String
“First 



be displayed beside 

name”



the input field.




data_points.required
Whether the user is
Boolean
true | false



required to provide this





information.




data_points.type
Used to identify 
Integer
0 | 1 | 2 | 



what type of

3 | 4



input field to display.





0 = Text field (EditText)





1 = List (Spinner)





2 = Checkboxes





3 = Radio buttons





4 = Switch




data_points.hint
A hint of what the user
String
“e.g., Bob”



should input. Only 





used for type 0.




data_points.options
Options to populate 
List<String>
[“Male”,



types 1, 2, and 3.

“Female”,





“Other”]


data_points.default
Whether the 
Boolean
true | false



Switch should be





set to true or false by





default.









Session Script
Filename: <id>.ss
S3 Location: data/session.scripts/<campaign-code>/
A session script can be thought of as a container for multiple prompts. A session script can be given a name, used internally, to help identify the type of prompts it contains, such as “POI-Seattle-WA-USA”.
Table 4 describes the content of a session script (“.ss”) file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.













Key
Description
Data Type
Example


















version
Schema version of 
Double
1.2



the .ss file.




id
A unique 12 character
String
“5C3D6D4E2981”



alphanumerical 





string used to





identify the 





session script.




name
Name given to 
String
“POI-Seattle-



the session.

WA-USA”



This won't be 





displayed to





the user. Used 





for internal





use.




language-
Language-culture 
String
“en-us”


culture
information





about the script. 





This may not





be used by The 





utterance collection 





application but for





post- processing.




prompts
Array that contains
List<Prompt>




a list of Prompts.




audio_config
Configurations 





for all the





audio files generated 





during the session. 





If this field is





missing, the 





app may use





the defaults provided





below.




audio_
The sample rate 
Integer
16000


config.sample_
for the audio




rate
recording. Default 





is 16000




audio_
The format for 
String
“WAV” | 


config.format
the audio

“RAW” |



recording. Default 

“PCM”



is “WAV”.




audio_
Integer representing 
Integer
1 = Mono


config.channel
mono or

2 = Stereo



stereo. Default is 1




audio_
The bit rate for 
Integer
16


config.bit_rate
the recording.





Default is 8.









Prompt
The Prompt JSON may be parsed by the application and used to create visual prompts for the user to make utterances with. This JSON defines the necessary fields needed for the initial prompt creation and display along with information used to linking prompts with their audio files and device information.
If session_type is “scripted”, then display_text must be defined. Otherwise, the Prompt may be rejected.
If session_type is “free-form” and audit_src may be ignored even if it is defined.
Table 5 describes the content of a prompt configuration. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.















Data 



Key
Description
Type
Example







id
A unique 20 character
String
“672616b39bab4cf4a162”



alphanumerical string 





used to identify the prompt.




utterance
The original 
String
“How is the traffic on



utterance provided

I-5?”



by a campaign 





administrator.




display_
The utterance to be 
String
“How is the traffic on


text
spoken by user. It is 

I-5?”



displayed as main





prompt to user.




tip
Used to help user properly
String
“Pronounce it has eye-



pronounce display_text.

five.”


session_
Represents whether the
String
“free-form” OR


type
Prompt is free-form or

“scripted”



scripted.




audit_src
Defines the manner 
String
“Google” | “VBT”



in which the Prompt 





should be audited.





If left blank, the Prompt 





may not be audited. 





Default is “VBT”




edit_
Word-for-word 
Double
  4.5


distance
difference tolerance 





threshold from what





ASR returns and the 





prompt's text.




min_
The minimum length, in
Long
2000


audio_
milliseconds, the audio




length
recording for this prompt 





can be. Default is 1000.









Logging
Filename: session.log
S3 Location: data/uploads/<campaign-code>/<device-id>/<session-script-id>/
Each session may have a log file generated and uploaded at the end of the session.
As the user progresses through the queue of prompts, utterance collection application 220 may collect statistical information, such as how long they spent a prompt, how many times they retried it, and how often they left the applications. At the end of the session, this log file may be uploaded into the same directory as the rest of the session's data.
The data collected may be in JSON format and should be structured accordingly.
Table 6 describes the content of a log file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.













Key
Description
Type
Example


















version
Schema version
Double
1.5



of the .log file.




origin.token
The token
String
“ABCDEF6A1”



entered by the





user.




origin.campaign_code
Campaign code
String
“ABCD”


origin.session_
Session script ID
String
“EF6A1F52587C”


script_id





origin.device_id
User's
String
“c6c479b4-3cf6-



device ID.

42ad-81a5-








prompts
List containing statistical info about each prompt.










prompts.pause
Total amount of
Long
12500



time, in





milliseconds, the





user paused on





given Prompt.




prompts.start_time
The time, in
Long
1408552410



epoch, when the





user started the





prompt.




prompts.duration
Total time spent
Long
35000



on a prompt, in





milliseconds.





Includes pauses.




prompts.end_time
The time, in
Long
1408552410



epoch, when the





user ended the





prompt.










prompts.json
The prompt's JSON. See Prompt section for details.










prompts.redos
Total amount of
Integer
4



times the user





redid a Prompt.










statistics
Statistical information for the entire session and all



prompts.


statistics.session
Statistical information about the entire session.










statistics.session.
The start time of
Long
1408552410


start_time
the session, in





epoch.




statistics.session.
Total time spent
Long
35000


duration
on the session, in





milliseconds.




statistics.session.
The time when
Long
1408552410


end_time
the user





completed the





session, in epoch.










statistics.prompts.
Information regarding redos done throughout the


redos
session.










statistics.prompts.
The average
Double
2.5


redos.average
number of redos





performed.




statistics.prompts.
Total number of
Integer
12


redos.total
redos done





throughout the





session.




statistics.prompts.
Information




redos.max
about the





maximum





amount of redos





done.




statistics.prompts.
The max number
Integer
25


redos.max.count
of redos.




statistics.prompts.
Prompt ID from
String
6f2544fb3fbf426998ab


redos.max.id
which the





maximum





number of redos





originated.




statistics.prompts.
Information




shortest
about the





shortest time





spent on a





prompt.




statistics.prompts.
Total amount of
Long
1500


shortest.duration
time, in





milliseconds,





spent on the





prompt.




statistics.prompts.
Prompt ID
String
e3335ab9a94748dfa163


shortest.id





statistics.prompts.
Information




longest
about the most





time spent on a





prompt.




statistics.prompts.
Total amount of
Long
60000


longest.duration
time, in





milliseconds,





spent on the





prompt.




statistics.prompts.
Prompt ID
String
bf3d601a35c74e098a49


longest.id





statistics.prompts.
Average amount
Long
45000


average_duration
of time, in





milliseconds,





spent on a





prompt.









Device Information
Filename: device.info
S3 Location: data/uploads/<campaign-code>/<device-id>/<session-script-id>/
When utterance collection application 220 is launched, it may collect information about the device, its location, and what version of the utterance collection application is being run. It may output this information into a .dev.info file as a JSON object.
The filename should be that of the device's UUID followed by the extension defined above. The file should then be uploaded into the upload folder as described within the In-Session Uploading section.
Table 7 describes the content of a device info file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.













Key
Description
Type
Example







version
Schema version of the
Double
1.5



.info file.










origin
Information about which, campaign, device, 



and session the file originated from.










origin.token
The token entered by the
String
“ABCDD210B”



user.




origin.campaign_
Campaign code
String
“ABCD”


code





origin.session_
Session script ID
String
“D210B4423984”


script_id





origin.device_id
User's device ID.
String
“c6c479b4-3cf6-





42ad-81a5-





49f6dd079c7e”


app_version
The utterance collection
String
0.1 SNAPSHOT 



application app version

2”



number.




board
The name of the 
String
“Goldfish”



underlying board.




bootloader
The system bootloader
String
“N7100XXUFNE1”



version number.




brand
The consumer-visible 
String
“samsung”



brand with which the





product/hardware may be





associated, if any.




cpu_abi
The name of 
String
“armeabi-v7a”



the instruction





set (CPU type + ABI





convention) of 





native code.




cpu_abi2
The name of the second
String
“armeabi”



instruction set 





(CPU type + ABI 





convention) of native





code.




display
A build ID 
String
“KOT49H.N7100X



string meant for

XUFNE1”



displaying to the user




fingerprint
A string that uniquely
String
“samsung/t03gxx/”



identifies this build.




hardware
The name of the 
String
“smdk4x12”



hardware (from the 





kernel command





line or /proc).




id
Either a change list 
String
“M4-rc20”



number or a label.




latitude
Latitude coordinates 
String
“47.6333578”



of the device's location.




longitude
Longitude coordinates of
String
“−122.1801977”



the device's location.




manufacturer
The manufacturer of the
String
“samsung”



product/hardware.




model
The end-user-visible 
String 
“GT-N7100”



name for the end product.




product
The name of the overall
String
“t03gxx”



product.




serial
A hardware serial 
String
“4df1218932298f0f”



number, if available.




tags
Comma-separated tags
String
“unsigned”



describing the build.




type
The type of build, like
String
“user”



“user” or “eng”.




version_
The internal value used 
String
“N7100XXUFNE1”


incremental
by the underlying source





control to represent this




version_release
The user-visible version
String
“4.4.2”



string.




version_sdk_
The user-visible SDK 
String
“19”


number
version of the framework.









User Information
Filename: user.info
S3 Location: data/uploads/<campaign-code>/<device-id>/<session-script-id>/
User information is gathered from the Demographic screen, which is derived from the .dem file. The data may be outputted in a JSON format using the following pattern keeping in mind that each .dem's data_point is the key and the value being the user's response.
Table 8 describes the content of a user info file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.















Data 



Key
Description
Type
Example







version
Schema version of the
Double
1.5



user. info file.










origin
Information about which, campaign, device, 



and session the file originated from.










origin.token
The token entered 
String
“ABCDD210B”



by the user.




origin.campaign_
Campaign code
String
“ABCD”


code





origin.session_
Session script ID
String
“D210B4423984”


script_id





origin.device_id
User's device ID.
String
“c6c479b4-





3cf6-42ad-





81a5-





49f6dd079c7e








data_points
List of label and selected options 



value derived from the .dem file and what the 



user inputted in the Demographic screen.










data_points.key
The label of the data_point
String
“First name”



from the .dem 





file for which the





user is providing an answer.




data_points.value
The value selected from the
String
“John”



options list for which the





user is providing an





answer.









Audio Files
Filename: <token>_<device-id>_<prompt-id>_<epoch-time>.<audio_config.format>
S3 Location: data/uploads/<campaign-code>/<device-id>/<session-script-id>/
Auditing
Filename: <device-id>.aud
S3 Location: data/audits/<campaign-code>
To maintain a high level of quality and block spammers, auditing on a prompt-level may be introduced and maintained by the applications.
If a particular prompt is to be audited, it may have its audit_src value set to “Google” and provide an edit_distance value greater than 0.
Edit Distance
The edit distance is used to measure how two phrases are different on a word-for-word basis. It is represented by a whole number (e.g., 10). Each Prompt may have its own edit distance provided by the Campaign Administrator.
Tracking Failed Audits
If a user fails an audit, it must be tracked on a per-campaign basis. This may be done by creating and updating an audit file (.aud) within data/audits/<campaign-code> every time a user fails an audit.
If the same device is reused, auditing should be turned off by omitting the failed_audits_threshold field within the .config file, setting the audit_src value, within a Prompt, to “VBT”, and omitting the edit_distance field.
Table 9 describes the content of an audit file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well.
















Data 




Key
Description
Type 
Example







version
Schema version for the
Double
1.6




.aud file.












failures
List containing information about prompts 




the device failed audit on.












failures.token
Token entered by
String
“58WEPO093”




user and from






where the failure






originated.





failures.campaign_code
Campaign code derived
String
“58WE”




from the token and from






where the failure






originated.





failures.session_script_id
The session script's ID from 
String

“PO0933C



which the failure originated.


1F560”


failures.prompt_id
The prompt's ID from which 
String

“3E75BA1



the failure originated.


EF145493






290E3”


failures.utterance
The utterance of the Prompt
String

“How is



from which the failure


the traffic



originated. This should be


on I-5?”



the same as the utterance






field of the prompt from the






session script file (.ss)





failures.audit_stt
The string returned by the
String

“How is



audit source (e.g., Google)


the traffic






on I5”









Handling & Maintaining Disqualification
If a user passes the failed_audits_threshold value while they are in the middle of a session, they may not be disqualified right away. Instead, they'll be allowed to complete the session and a completion code (if generate_completion_code is set to true in .confg file).
The failed_audits_threshold value check is done when the user first enters the token code at the beginning of app launch. Here is how the application should check:
Check data/audits/<campaign-code> for a .aud file with the same filename as the device's ID
If one exists, download the file, parse the JSON within, and count the JSON array.
If the size of the array is greater-than the failed_audits_threshold, then the user is disqualified from participating in the campaign and is shown a disqualification page.
Completion Codes
If generate_completion_code is set to true within the .config file, at the end of a successful completion of a session, the application may generated a unique 8-character alphanumerical string. The Completion Code may be displayed to the user who can then enter it on the crowdsourcing website to validate that they've successfully completed a single session.
In addition, the Completion Code may be stored for later retrieval. The three cases in which the Completion Code made be retrieved are described below.
Crowdsourcing Validation
Within data/completion_codes/<campaign-code> folder, an empty file named after the completion code is uploaded.
This bucket is later queried by an automation script developed by the Campaign Administrator to validate, in real-time, whether the completion code entered by the user matches any of the empty text files within the bucket. If one exists, validation passes and the file is removed to ensure that the same completion code cannot be reused.
Post-Processing
During post-processing, if one needs to know the Completion Code given to the participate at the end of a session, the completion code generated by the application may be stored with the same name as the completion code (e.g., “KSYUV2DU”).
The file may be stored on the following location on S3:
data/uploads/<campaign-code>/<device-id>/<session-script-id-plus-epoch-timestamp>
Application Retrieval
Utterance collection application 220 may allow a user to retrieve all the Completion Codes generated by them over a course of several sessions per campaign.
This may be done by maintaining a JSON file named completion_codes.cc. As the user completes a session and the application generated the Completion Code, it may be appended to the JSON.
Table 10 describes the content of a completions codes file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well. The file may be saved within the following bucket, ensuring that just one file exists for each device and each campaign:
data/uploads/<campaign-code>/<device-id>/















Data 



Key
Description
Type
Example







token
Token entered by user 
String
“58WEPO093”



and for which the





completion code 





was generated.




campaign_code
Campaign code derived 
String
“58WE”



from the token and





which the completion 





code was generated.




session_script_id
The session script's 
String
“PO0933C1F560”



ID for which the





completion code 





was generated.




session_name
The session script's 
String
“Session 0001”



name from which the





completion code 





was generated.




completion_code
The 8-digit alphanumerical 
String
“ADXGCAKW”



Completion Code





generated by the app at the 





end of the session Script.









In-Session Uploading
Each time a Prompt is completed and the user hits the “Next” button to load the next Prompt in the queue, utterance collection application 220 may automatically upload the audio file onto S3.
The added benefit of having in-session uploading, is that a Campaign Administrator can monitor, in real-time, the progress of a particular user.
Storage Structure
Table 11 describes the an exemplary storage scheme. The following table is provided for illustrative purposes and should not be construed as limiting, as other storage schemes (as described herein and may be apparent based on the instant disclosure) may be used as well.











Path
Definition







audits/
Where a <device-id>. aud file is stored 


<campaing_code>
tracking the number of



audits a user has failed. See Auditing section.


completion.codes/
Where an empty file, named after the Completion 


<campaign-code>
Code generated at the end of a session, 



is stored. They are stored



within a sub-directory named after 



the corresponding campaign's



code. These files are used during the 



Crowdsourcing Validation phase.


demographics/
Where .dem files are stored. These 


<demographic>.dem
files are used to construct the



Demographic page in The utterance 



collection application. The



name of the .dem file is provided in 



the demographic field within



the .config JSON. See Demographics 



for JSON structure details



and Configuration for



.config details.


session.scripts/
Where session Script files (.ss) are 


<session_script_dir>
kept. The sub-directory, in



which these files are kept, is 



defined in the .config file's



session_script_dir field.


settings
Where the .config is kept. 



These files are named after the campaign code, 



which is derived from the token. See Configuration 



for JSON structure and field definitions.


tutorials/
Where .tut files are stored. These 


<tutorial>.tut
are used to construct the



various Tutorial pages in The utterance collection



application. The filename for them is given 



within the .config file.


uploads/
Where all files associated with a 


<campaign-
single session are uploaded.


code>/
These files include:


<device-id>/
     Audio files (.wav)


<session-script-
     Demographics files (.dem and user.info)


id-timestamp>
     Device information (device.info).



     Tutorials (.tut).



     Configuration (.config).



     Session Script (.ss).



     Completion code









External Device Storage
Sometimes, a Campaign Administrator may not want completed session information to be uploaded to our S3 bucket. Instead, they want to be able to retrieve it from the device itself. In order to achieve this, the external_storage field in the .config file of the campaign should be set to true.
When this is done, all audio files, demographic information, and other user-generated data is stored within the phone's external storage. The file structure mimics the one found under the data/uploads folder, as described above, with one key difference; the <device-id> folder is removed and everything below is moved up one directory.
So instead of having this file structure (from S3):
uploads/<campaign-code>/<device-id>/<session-script-id-timestamp>
It would be:
the utterance collection application/<campaign-code>/<session-script-id-timestamp>
File Compression
In order to save space on devices where multiple sessions and campaigns are run, after a user successfully completes a session, all the files within the /<session_script_id_timestamp> may be zipped into a .zip file originally introduced in The utterance collection application 1.0.
The filename may be structured as such:
device-id_session-id_epoch-time.zip
Calibration
Some Campaign Administrators may want the campaigns, and its sessions, to be conducted under a quite environment with minimal ambient noise. In order to ensure this is the case, The utterance collection application has a new Calibration features.
When enabled, the utterance collection application may display a Calibration UI before the beginning of a session. In the UI, the user may be instructed to hold the phone at an optimal distance and in the correct form. There may be a horizontal VU meter with green, yellow, and red sections. It may, in real-time, illustrate the noise being picked up by the device's microphone.
The Calibration feature can be enabled by settings the do_calibration field (within .config) to true. Furthermore, the green, yellow, and red ranges of the VU meter can be defined through the db_levels field in the same configuration file.
The use of the VU meter may help in two ways:
It may help the user get a better understanding of whether their environment is quiet enough to produce clear and noise-free recordings.
It lets the user know whether their voice is loud enough to be picked up by their device's microphone.
The actual design of the VU meter should be very simple. It should consist of a vertical meter bar that light up as the noise level increases—with the lights building on top of one another.
Green—the noise level is acceptable.
Orange—the noise level is getting high.
Red—the noise level is unacceptable. During calibration, this should prevent the user from advancing forward.
The VU meter should measure the environment's volume for 10 seconds, which may be considered the calibration period. During this time, the meter should fluctuate, in real-time, with the volume level of the environment—it should not be static. This may guarantee a nice user experience and real-time feedback to the user and application.
In addition, during the calibration period, if the meter reaches red and stays there for more than 3 seconds, the time should restart and warn the user that their environment is too loud and that they cannot proceed any further until 10 seconds have elapsed with the meter remaining within the green and orange levels.
The user may not be able to progress to the first session until they are able to successfully complete the Calibration screen.
Versioning
Filename: manifest.mf
S3 Location: uploads/<campaign-code>/<device-id>/<session-script-id-timestamp>
All JSON files consumed and produced by the application must have a version field. This may aid automation scripts in the post-processing phase. In particular, it may help identify what data a particular file contains (or does not contain) by their version number.
It is important that every time a field is added, removed, or adjusted within a file, the version number must be incremented.
In addition, every time an upload folder is created on S3 or within a device (if external_storage is set to true in the .config file), a manifest file may be added which may include a list of all the files to be found within the folder and their version.
The manifest file may be a JSON containing an array of name, extension, and version fields.
Table 12 describes the content of a manifest file. The following table is provided for illustrative purposes and should not be construed as limiting, as other content may be used as well. The file may be saved within the following bucket, ensuring that just one file exists for each device and each campaign:















Data 



Key
Description
Type
Example







version
Version of the manifest file.
Double
1.2








files
List containing versioning information 



of each file within the upload folder.










files.name
Proper name of the file.
String
“Session





Script”


files.extension
The file extension.
String
“.ss”


files.version
The version of the mentioned 
Double
1.5



file. This should be taken 





from the file's version field.









Edge-Case & Error Handling
Table 13 outlines how the utterance collection application should behave if certain scenarios are encountered. The following table is provided for illustrative purposes and should not be construed as limiting, as other scenarios/resolutions may be used as well.












Scenario
Issue
Mitigation







1
User is disqualified 
The application may 



because they have
display either a screen or a



exceeded the
dialog explaining that 



failed_audits_
they have exceeded the



threshold value in the
maximum allowed 



.config file. 
audit failures and cannot




participate in this 




particular campaign.


2
User has used up 
The application may display 



the allotted
either a screen or a



quota of sessions per 
dialog explaining that they 



campaign.
have exceeded the




maximum allowed number 




of sessions per user for




the given campaign.


3
User does not have 
The user is presented with a 



network connectivity 
dialog indicating that



on app launch.
they must connect to the 




internet before proceeding.




Clicking “OK” on the dialog 




may take them to their




device's Network 




Connection settings.


4
During a Prompt's 
The user is not prompted



audio file upload, the
when this happens.



user loses network 
Instead, at the end of the 



connectivity.
session, the application




may check the upload bucket 




against locally stored




files. If any local files are not 




on S3, it may attempt to




upload them.


5
Silence detection does 
One of two things can happen:



not detect silence
  The user can manually 



and the recording 
  press the ‘Stop



continues.
  Recording’button.




  The max_audio_recording 




  limit is hit and the app




  automatically stops 




  recording.


6
The user fails too 
Nothing is displayed or 



many audits while
indicated to the user. Instead,



in session.
in the background, the audit 




count, described in




Tracking Failed Audits, is 




quietly incremented.









The one or more processors 212 illustrated in FIG. 2 may each include one or more physical processors that are programmed by computer program instructions. The various instructions described herein are exemplary only. Other configurations and numbers of instructions may be used, so long as the processor(s) 212 are programmed to perform the functions described herein.
Furthermore, it should be appreciated that although the various instructions are illustrated in FIG. 2 as being co-located within a single processing unit, in implementations in which processor(s) 212 includes multiple processing units, one or more instructions may be executed remotely from the other instructions.
The description of the functionality provided by the different instructions described herein is for illustrative purposes, and is not intended to be limiting, as any of instructions may provide more or less functionality than is described. For example, one or more of the instructions may be eliminated, and some or all of its functionality may be provided by other ones of the instructions. As another example, processor(s) 212 may be programmed by one or more additional instructions that may perform some or all of the functionality attributed herein to one of the instructions.
The various instructions described herein may be stored in a storage device 214, which may comprise random access memory (RAM), read only memory (ROM), and/or other memory. The storage device may store the computer program instructions (e.g., the aforementioned instructions) to be executed by processor 212 as well as data that may be manipulated by processor 212. The storage device may comprise floppy disks, hard disks, optical disks, tapes, or other storage media for storing computer-executable instructions and/or data.
The various databases described herein may be, include, or interface to, for example, an OracleTM relational database sold commercially by Oracle Corporation. Other databases, such as InformixTM, DB2 (Database 2) or other data storage, including file-based, or query formats, platforms, or resources such as OLAP (On Line Analytical Processing), SQL (Structured Query Language), a SAN (storage area network), Microsoft AccessTM or others may also be used, incorporated, or accessed. The database may comprise one or more such databases that reside in one or more physical devices and in one or more physical locations. The database may store a plurality of types of data and/or files and associated data or file descriptions, administrative information, or any other data.
The various components illustrated in FIG. 1 may be coupled to at least one other component via a network 107, which may include any one or more of, for instance, the Internet, an intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a SAN (Storage Area Network), a MAN (Metropolitan Area Network), a wireless network, a cellular communications network, a Public Switched Telephone Network, and/or other network. In FIG. 1, as well as in other drawing Figures, different numbers of entities than those depicted may be used. Furthermore, according to various implementations, the components described herein may be implemented in hardware and/or software that configure hardware.
The various processing operations and/or data flows depicted in FIG. 3 (and in the other drawing figures) are described in greater detail herein. The described operations may be accomplished using some or all of the system components described in detail above and, in some implementations, various operations may be performed in different sequences and various operations may be omitted. Additional operations may be performed along with some or all of the operations shown in the depicted flow diagrams. One or more operations may be performed simultaneously. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
Other implementations, uses and advantages of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. The specification should be considered exemplary only, and the scope of the invention is accordingly intended to be limited only by the following claims.

## Claims

1. A computer-implemented method of recording utterances from unmanaged crowds for natural language processing, the method being implemented in a user device having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the user device to perform the method, the method comprising:
obtaining, by the user device, a token;
transmitting, by the user device, the token to a remote device via a network;
receiving, at the user device, from the remote device, one or more utterances to be uttered by a user and one or more campaign configuration parameters based on the token, wherein the one or more utterances and the one or more campaign configuration parameters are associated with a campaign that is associated with a natural language processing data collection effort;
configuring, by the user device, the computer program instructions based on the one or more campaign configuration parameters;
presenting to the user, by the user device, the one or more utterances to be uttered by the user; and
recording, by the user device, audio of the user uttering the one or more utterances.

2. The method of claim 1, wherein the token comprises an alphanumeric string.

3. The method of claim 2, wherein the alphanumeric string includes a campaign identifier that is associated with the one or more campaign configuration parameters.

4. The method of claim 2, wherein the alphanumeric string includes a session script identifier that is associated with the one or more utterances to be uttered.

5. The method of claim 3, wherein the one or more campaign configuration parameters include a configuration parameter that indicates that an audit check should be performed, the method further comprising:
configuring, by the user device, the computer program instructions to determine whether a number of failed audits associated with the campaign identifier with which the user device was involved exceeds a predetermined threshold number of failed audits; and
responsive to a determination that the number of failed audits with which the user device was involved exceeds the predetermined threshold number of failed audits, preventing the user device from participating in additional utterance recordings associated with the campaign identifier.

6. The method of claim 1, wherein the one or more campaign configuration parameters include a calibration parameter that indicates that a calibration test should be performed, the method further comprising:
configuring, by the user device, the computer program instructions to perform a calibration test prior to presenting to the user the one or more utterances, by:
determining, by the user device, a level of ambient noise; and
determining, by the user device, whether the level of ambient noise satisfies the calibration test; and

presenting to the user, by the user device, the one or more utterances to be uttered by the user responsive to a determination that the ambient level of audio satisfies the calibration test.

7. The method of claim 6, wherein the calibration parameter specifies a minimum level of ambient noise to satisfy the calibration test.

8. The method of claim 6, wherein the calibration parameter specifies a maximum level of ambient noise to satisfy the calibration test.

9. The method of claim 1, further comprising:
generating, by the user device, a completion code responsive to a determination that audio has been recorded of the user uttering each of the one or more utterances.

10. The method of claim 1, further comprising:
causing, by the user device, the recorded audio to be provided to the remote device via the network.

11. The method of claim 1, wherein obtaining a token comprises:
capturing, by the user device, an image of a machine-readable code, wherein the token is encoded in the machine-readable code.

12. The method of claim 1, wherein obtaining a token comprises:
receiving, by the user device, a user input comprising an alphanumeric string that comprises the token.

13. The method of claim 1, wherein transmitting the token to a remote device comprises:
transmitting, by the user device, the token to a transcription system that manages multiple campaigns associated with a natural language processing data collection effort.

14. The method of claim 1, wherein transmitting the token to a remote device comprises:
transmitting, by the user device, the token to a crowd-sourcing service.

15. The method of claim 1, further comprising:
causing, by the user device, recorded audio of the one or more utterances to be provided to the remote device via the network after the user has uttered all of the one or more utterances.

16. The method of claim 1, further comprising:
causing, by the user device, recorded audio of each of the one or more utterances to be provided to the remote device individually via the network after the user has uttered each of the one or more utterances.

17. A system for recording utterances from unmanaged crowds for natural language processing, the system comprising:
a user device having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the user device to:
obtain a token;
transmit the token to a remote device via a network;
receive, from the remote device, one or more utterances to be uttered by a user and one or more campaign configuration parameters based on the token, wherein the one or more utterances and the one or more campaign configuration parameters are associated with a campaign that is associated with a natural language processing data collection effort;
configure the computer program instructions based on the one or more campaign configuration parameters;
present to the user the one or more utterances to be uttered by the user; and
record audio of the user uttering the one or more utterances.

18. The system of claim 17, wherein the token comprises an alphanumeric string.

19. The system of claim 18, wherein
the alphanumeric string includes a campaign identifier that is associated with the one or more campaign configuration parameters.

20. The system of claim 18, wherein
the alphanumeric string includes a session script identifier that is associated with the one or more utterances to be uttered.

21. The system of claim 19, wherein the one or more campaign configuration parameters include a configuration parameter that indicates that an audit check should be performed, and wherein the user device is further programmed to:
configure the computer program instructions to determine whether a number of failed audits associated with the campaign identifier with which the user device was involved exceeds a predetermined threshold number of failed audits; and
responsive to a determination that the number of failed audits with which the user device was involved exceeds the predetermined threshold number of failed audits, prevent the user device from participating in additional utterance recordings associated with the campaign identifier.

22. The system of claim 17, wherein the one or more campaign configuration parameters include a calibration parameter that indicates that a calibration test should be performed, and wherein the user device is further programmed to:
configure the computer program instructions to perform a calibration test prior to presenting to the user the one or more utterances, wherein to perform the calibration test, the user device is further programmed to:
determine a level of ambient noise; and
determine whether the level of ambient noise satisfies the calibration test; and

present to the user the one or more utterances to be uttered by the user responsive to a determination that the ambient level of audio satisfies the calibration test.

23. The system of claim 22, wherein the calibration parameter specifies a minimum level of ambient noise to satisfy the calibration test.

24. The system of claim 22, wherein the calibration parameter specifies a maximum level of ambient noise to satisfy the calibration test.

25. The system of claim 17, wherein the user device is further programmed to:
generate a completion code responsive to a determination that audio has been recorded of the user uttering each of the one of one or more utterances.

26. The system of claim 17, wherein the user device is further programmed to:
cause the recorded audio to be provided to the remote device via the network.

27. The system of claim 17, wherein, to obtain the token, the user device is further programmed to:
capture an image of a machine-readable code, wherein the token is encoded in the machine-readable code.

28. The system of claim 17, wherein, to obtain the token, the user device is further programmed to:
receive a user input comprising an alphanumeric string that comprises the token.

29. The system of claim 17, wherein, to transmit the token, the user device is further programmed to:
transmit the token to a transcription system that manages multiple campaigns associated with a natural language processing data collection effort.

30. The system of claim 17, wherein, to transmit the token, the user device is further programmed to:
transmit the token to a crowd-sourcing service.

31. The system of claim 17, wherein the user device is further programmed to:
cause the recorded audio to be provided to the remote device via the network after the user has uttered all of the one or more utterances.

32. The system of claim 17, wherein the user device is further programmed to:
cause recorded audio of each of the one or more utterances to be provided to the remote device individually via the network after the user has uttered each of the one or more utterances.