Patent Publication Number: US-11392771-B2

Title: Methods for natural language model training in natural language understanding (NLU) systems

Description:
BACKGROUND 
     The present disclosure relates to natural language model training systems and methods and, more particularly, to systems and methods related to training and employing natural language models in natural language understanding (NLU) systems operations. 
     SUMMARY 
     No doubt, voice-controlled human machine interfaces have gained notoriety among avid electronic device users. Learning to recognize and process speech, however, is not an easy feat for these interface devices. Large data sets serve as training input to speech recognition models to facilitate reliable speech recognition capability over time, oftentimes over a long time. Generally, the larger the training data set and the longer the training, the more reliable the recognized speech. Correspondingly, text string recognition capability shares similar reliability characteristics. Voice and/or text string recognition technology for certain applications remain in their infancy with improvements yet to be realized. Regardless of the training size or training duration, speech and text recognition suffer from inaccuracies when provided with inputs of inadequate clarity and volume. A soft-spoken voice often falls victim to misinterpretation or no interpretation by a device having voice interface capabilities. Take the case of a 6-year old child for example. Speaking to a device, located 10 or 20 feet away, the 6-year old is unlikely to speak with requisite voice strength and speech clarity for proper speech or text recognition functionality. Unless spoken with clarity and particularly strength of volume, a device using voice input does not and cannot carry out the child&#39;s commands, for example. Children are naturally made to speak louder to properly convey their wishes, an outcome that is not without consequence. Habits generally start to take form at an early age, and current voice-recognition technology albeit unintentionally is teaching kids to learn to behave rudely and obnoxiously by loudly voicing a command. 
     Voice-recognition technology manufacturers have attempted to address the foregoing issue by requiring devices with voice interfaces to conform to polite speech, for example, “thank you” or “please” preceding or following a command, such as “change channels” or “play Barney”. In some cases, the device will simply refuse to carry out the command in the absence of detecting an obsequious expression. The Amazon&#39;s Echo device, Amazon Fire TV, Amazon Fire Stick, Apple TV, Android mobile devices with Google&#39;s “Ok Google” application and the iPhone with Sin serve as examples of devices with voice interface functionality. Some devices go as far as responding to an impolite input query only to remind the user to repeat the command using polite words and not until a polite command follows will the device indeed carry out the command. In response to “play Barney”, for example, the device prevents the show Barney from playing until an alteration of the command is received using an obsequious expression, i.e. “play Barney, please”. Such advancements are certainly notable but other issues remain. 
     Natural language voice recognition systems, such as natural language understanding (NLU) systems, require user utterance training for proper utterance matching in addition to user query recognition and interpretation functionalities. Adding an obsequious expression to a user query as a prefix or a suffix, such as “please” at the end of “play Game of Thrones”, presents challenges to voice-recognition model training. One such challenge is a reduction in match scores of previously trained speeches (or queries). Simply put, in the presence of an obsequious expression, the model fails to recognize an utterance with an equivalent degree of accuracy as its predecessors. Consequently, additional costly and lengthy training techniques may be required. Further, system architecture is made unnecessarily complicated to accommodate additional natural language model training for text strings or speech that include obsequious expressions. Finally, removing obsequious expressions from search queries, while a seemingly viable solution, poses a problem relative to content search applications with entity titles that include such expressions, because removing the expressions from the query yields poor results. For example, the movie title, “Play Thank You for Smoking”, may be reduced to “Play&gt;entity_title&lt;you for smoking&gt;”, which would yield incorrect results. Some of the examples presented in this disclosure are directed to determinations for including, or not, obsequious expressions, however, it is understood that some embodiments of the disclosure may be used for ease of training a model to understand expressions, other than obsequious expressions. In some embodiments, suitable expressions for the purpose of training a model, for example, help to decrease the functionality of the NLU system, are contemplated. 
     To overcome the preceding limitations, the present disclosure describes a natural language model-based voice recognition system that facilitates speech recognition with reduced model training sets while meeting the precision certainty of legacy voice recognition systems. Model training is implemented with minimal system architecture alterations to promote plug-and-play modularity, a design convenience. 
     In disclosed embodiments and methods, a natural language model of a natural language understanding (NLU) (also referred to as “natural language processing (NLP)”) system is minimally trained and conveniently adaptable for legacy system compatibility. The model can be made to operate with existing natural language-based voice recognition systems, it requires a mere design-convenient plug-and-play implementation. In some embodiments, the model facilitates a simple binary prediction classification, trained to recognize a query with an obsequious expression and a query without an obsequious expression, for example. 
     In some embodiments, a query is generated using a trained natural language model in an NLU system. The query is tested to include an obsequious expression, or not. In some embodiments, a query may contain a user prescribed action and the model is trained to determine to perform the prescribed action, or not. In some embodiments, the model is trained to recognize child-spoken speech or correspondingly text string generated from child-spoken speech. 
     In some embodiments, the NLU system is pre-processing (or pre-training) assisted. A classifier binary model implements a simple classification prediction to generate queries for the NLU system. In some embodiments, the classifier binary model facilitates query generation. For example, the model may be trained with command text string queries or non-command text string queries, “play Game of Thrones” or “thank you for smoking”, respectively. In operation, the trained model facilitates text string query recognition by offering pre-processing assistance to a natural language understanding processor for sentence recognition, for example. 
     The query text string may include one or more content entities. In some embodiments, the text string may correspond to user originated speech (or audio), and the content entity may correspond to a command. For example, a voice command may be transcribed into a text string: “Play Barney” or “Show me the Game of Thrones”. The system determines whether the text string includes an obsequious expression, for example, does the text string “Play Barney” include the term “please”, or does the text string “Play Barney, please!” include the term “please”? 
     In some embodiments, the system may make a contextual determination of the obsequious expression. In this connection, the binary model may be trained to recognize contextualized natural language. In some embodiments, in response to an obsequious expression descriptor determination, the system may treat the obsequious expression as a part of the text string. For example, the string “Thank you for smoking!” includes the obsequious term, “thank you”, yet the system determines the term is an unintended obsequious expression (a title of a movie), one that describes the remainder of the text string, “for smoking!”. 
     In some embodiments, in response to determining whether the text string includes an obsequious expression during pre-processing, the system determines to forward the query to the remaining components of the NLU system, such as a NLU processor, based on a determination as to whether the obsequious expression describes the content entity. In response to determining the obsequious expression describes the content entity, the query may be forwarded with the obsequious expression and in response to determining the obsequious expression does not describe the content entity, the query may be forwarded without the obsequious expression. In this manner, the input to a subsequent natural language recognition processor are matched against known elements and legacy match scores remain unchanged. 
     In some embodiments, in response to receiving a text string with a content entity, a determination is made regarding the text string. If the determination yields the text string includes an obsequious expression, the system further determines whether the obsequious expression describes the query content entity. In response to determining the obsequious expression describes the content entity, the query is generated with the content entity and the obsequious expression and in response to determining the obsequious expression does not describe the content entity, the query is generated with the content entity but without the obsequious expression. For example, the text string “play Game of Thrones” is tested for including an obsequious expression (e.g., “please” or “thank you”). If the text string is determined to include an obsequious expression but the obsequious expression is contextually not an intended obsequious word or expression (e.g., “thank you for smoking”, the title of a movie), the query is generated with the obsequious expression and if the text string includes an obsequious expression and the obsequious expression is intentional, i.e. intentional use of a polite word or expression, the query is generated without the obsequious expression to maintain query prediction integrity (legacy match scores). As referenced herein, an “expression” is synonymous with a “term” or one or more “words”. For example, an “obsequious expression” is synonymous with “obsequious term”, and “obsequious word(s)”. 
     The binary model may be trained with obsequious expressions or without obsequious expressions. For example, in cases where an obsequious expression is detected and the detected obsequious expression does not describe the content entity, the binary model may be trained with a presence of an obsequious expression or with the absence of an obsequious expression. Correspondingly, in cases where an obsequious expression is detected and the detected obsequious expression does describe the content entity, the binary model may be trained with a presence of an obsequious expression or with the absence of an obsequious expression. As used herein, detecting or determining the presence of an entity correspondingly applies to detecting or determining the absence of the entity. For example, reference to detecting or determining the presence of an obsequious expression correspondingly applies to detecting or determining the absence of the obsequious expression and reference to detecting or determining an obsequious expression describing a content entity correspondingly applies to detecting or determining the absence of the obsequious expression describing the obsequious expression. 
     Noted earlier, in some embodiments, a determination is made to perform an action prescribed in the query using the trained binary model. The query is received with a content entity including a text string prescribing the action. In the above-noted embodiments and methods, the text string corresponds to an audio (or voice) input but in the case of determining to perform an action, or not, the system may make an additional determination relating to the audio input—the system may determine whether the query text string corresponds to an audio input from a categorized group based on the input spectral characteristics and audio features. A group may be categorized (or classified) as an adult, child, or unknown group, or based on other suitable grouping classifications including, without limitation, demographic or geographic. In response to determining the text string corresponds to an audio input from a group categorized as a “child”, for example, the system further determines whether the text string includes an obsequious expression. In the case of determining the presence of an obsequious expression in the text string and detecting a child voice, the system determines to perform the action and in the case of determining the absence of an obsequious expression in the text string and detecting a child voice, the system determines to not perform the prescribed action. For example, if the system detects the text string “play Barney” from a child voice, the system determines to not play Barney and if the system detects the text string “play Barney, please” from a child voice, the system determines to play Barney. 
     In the case of determining the presence of an obsequious expression in the text string and detecting a child voice, the system may further determine whether the obsequious expression describes the content entity. In the case of determining the presence of an obsequious expression in the text string, detecting a child voice, and determining the obsequious expression does not describe the content entity, the system determines to perform the action. In the case of determining the absence of an obsequious expression in the text string and detecting a child voice and determining the obsequious expression does not describe the content entity, the system determines to not perform the prescribed action. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects and advantages of the disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which: 
         FIGS. 1-4  are illustrative examples of natural language understanding (NLU) systems, in accordance with some disclosed embodiments of the disclosure. 
         FIGS. 5-9  depict illustrative flowcharts of query generation and determination processes, in accordance with some embodiments of the disclosure; 
         FIG. 10  is an illustrative block diagram showing a natural language recognition system, in accordance with some embodiments of the disclosure; and 
         FIG. 11  is an illustrative block diagram showing an NLU system incorporating query generation and model training features, in accordance with some embodiments of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a natural language understanding (NLU) system, in accordance with various disclosed embodiments and methods. In  FIG. 1 , a natural language understanding (NLU) system is configured as a natural language understanding (NLU) system  100 , in accordance with various disclosed embodiments and methods. NLU system  100  may implement query generation and natural language model training features. NLU system  100  may alternatively or additionally implement prescribed action query determination and query response features. 
     In  FIG. 1 , NLU system  100  is shown to include a device  102 , in accordance with various disclosed embodiments and methods. In some embodiments, device  102  comprises voice control capabilities. Device  102  may include, as shown in the embodiment of  FIG. 1 , a classifier binary model  104 , and a content database  106 , in accordance with disclosed embodiments. Classifier binary model  104  and content database  106  collectively comprise a natural language model training pre-processing unit (or “pre-training unit”)  150 . In some embodiments, device  102  may join the collection as a part of the pre-processing unit  150 . In embodiments with part or all of the relevant functions of classifier binary model  104 , device  102 , or a combination performed by network elements of a communication network (e.g., a network cloud), as will be further discussed below, pre-processing unit  150  may comprise at least part of the communication network elements performing the relevant pre-processing functions. For example, pre-processing unit  150  may include components or combinations of components performing each of processes  500  through  800  of  FIGS. 5-8 , respectively. 
     Pre-processing unit (or pre-training unit)  150  assists in natural language model training and facilitates natural language model training operations. In some embodiments, pre-processing unit  150  generates a query to assist with simplifying natural language model training. In some embodiments, pre-processing unit  150  assists with determining to perform certain functions and operation, such as, without limitation, a prescribed action, using the natural language model. In the embodiments of  FIGS. 1-4 , corresponding pre-processing unit outcomes are provided to an NLU processor, such as, without limitation, an NLU processor of  FIG. 10 , for natural language model training. 
     In some embodiments, content database  106  may manage stored content entities of a content entity data structure  130 . A content entity data structure, such as but not limited to content entity data structure  130 , may include one or more content entities. 
     In  FIG. 1 , content database  106  is shown to include a single content entity data structure but it is understood that more than one content entity may be housed and managed by content database  106 . A content entity is a grouped content based on a common type or a common category—an entity. For example, in the presented content entity of content entity data structure  130 , entities “Game of Thrones” and “Barney” share a common category of tvseries, content media candidates of a media device. Stated differently, content is tagged by content entity in content entity data structure  130  based on, for example, content entity type, Play ENTITY_tvseries. Nonlimiting examples of entities of the content entity Play ENTITY_tvseries are television series, “The Big Bang Theory” (not shown in  FIG. 1 ), “Game of Thrones” (shown in  FIG. 1 ) and “Barney” (shown in  FIG. 1 ). 
     Device  102  receives voice (or speech) input  118  and generates a responsive query for transmission to classifier binary model  104 . For example, a user queries device  102 , for a media content (e.g., Game of Thrones), and the electronic device provides the media content that best matches the user&#39;s query. Device  102  may be responsive to more than one voice input, such as voice input  120 . In practical applications, device  102  is generally responsive to many voice inputs. 
     As referred to herein, the term “media content” and “content” should be understood to mean an electronically consumable content by a user, such as online games, virtual content, augmented or mixed reality content, direct-to-consumer live streaming, virtual reality chat applications, virtual reality video plays, 360 video content, a television or video program, internet content (e.g., streaming content, downloadable content, webcasts, . . . ), video clips, audio, content information, pictures, images, documents, playlists, websites, articles, e-books, blogs, chat sessions, social media, applications, games, and/or any other media or multimedia and/or combination thereof. 
     Device  102  implements a speech-to-text transcription to convert voice input to a text string for natural language model training and natural language model operation applications. Device  102  may implement automatic speech recognition (ASR) to facilitate speech-to-text transcription. In the example of  FIG. 1 , device  102  transcribes voice input  118  to text string  132  and transcribes voice input  120  to text string  134 . 
     Transcription of voice input  118  or  120  may be achieved by external transcription services. In a nonlimiting example, in response to receiving voice input  118  or voice input  120 , at a receiver  116 , device  102  transmits the received voice input to an external ASR service for speech-to-text transcription and in response, receives text strings  132  and  134 , respectively. Nonlimiting examples of ASR services are Amazon Transcribe by Amazon, Inc. of Seattle, Wash. and Google Speech-to-Text by Google, Inc. of Mountain View, Calif. 
     Device  102  implements a contextual voice recognition feature for natural language construct of text strings from voice input  118  or voice input  120 . Device  102  may determine whether a part of a text string describes the remainder or a remaining portion of the text string. For example, an obsequious expression, such as “thank you” in text string  132  may actually describe, relate to or associate with the remainder of the text string “for smoking” and not intended as an obsequious expression, the content entity. In nonlimiting examples, device  102  may employ vector quantization (VQ) techniques employing its distinct codebook or based on a single universal (common) VQ codebook and its occurrence probability histograms natural language recognition techniques and algorithms. In some embodiments, rule-based language processing techniques may be employed. In some embodiments, statistical natural language processing techniques may be employed. In some natural language recognition models, grammar induction and grammar inference algorithms, such as context-free Lempel-Ziv-Welch algorithm or byte-pair encoding and optimization, may be employed. Lemmatization tasks may be employed to remove inflectional endings, morphological segmentation may be performed to separate words into individual morphemes and identify the class of morphemes, part-of-speech tagging, parsing, sentence boundary disambiguation, stemming, word segmentation, terminology extraction, and other suitable natural language recognition techniques. In example embodiments, natural language recognition processes may be implemented with speech recognition algorithms such as hidden Markov model, dynamic time warping, and artificial neural networks may be employed. 
     In some embodiments, each of the components shown in  FIG. 1  may be implemented in hardware or software. For example, classifier binary model  104  may be implemented in hardware or software. In cases implementing classifier binary model  104  in software, a set of program instructions may be executed and when executed by a processor cause binary model  104  to perform functions and processes as those disclosed herein. Similarly, device  102  may be implemented in hardware or software, and in the latter case, such as by a set of program instructions that when executed by a processor cause device  102  to perform functions and processes as those disclosed herein. Content database  106  may also be implemented in hardware or software, and in the latter case, such as by a set of program instructions that when executed by a processor cause content database  106  to perform functions and processes, such as those disclosed herein. In some embodiments, processing circuitry  1140  of control circuitry  1128  of a computing device  1118  or processing circuitry  1126  of control circuitry  1120  of a server  1102  ( FIG. 11 ) may execute program instructions to implement functionality of classifier binary model  104 , device  102 , content database  106 , or a combination. In an example application, processing circuitry  1040  may execute program instructions stored in a storage  1138  and processing circuitry  1126  may execute program instructions stored in a storage  1124 . 
     In some embodiments, device  102  is an electronic voice recognition (or voice-assisted) device that may be responsive to user voice commands, such as voice input  118  and  120 . Device  102  receives voice input in the form of audio or digital signals (or audio or digital input). In some embodiments, device  102  receives voice input at receiver  116 . In some embodiments, device  102  recognizes voice input only when prefaced with an expected phrase such as an action phrase. For example, device  102  may be an Amazon Echo or a Google Home device that recognizes user voice commands such as “Play Game of Thrones” or “Thank you for smoking!” when the user voice commands are prefaced with distinct and known action phrases, “Alexa” or “Ok, Google”, respectively. In a practical example, a user may utter “Alexa, Play Game of Thrones” or “Ok, Google, Play Game of Thrones” based on the manufacturer design of the device. Voice-assisted input  102  may be responsive to an action phrase other than “Ok, Google”, “Siri”, “Bixby” or “Alexa,”. In some embodiments, device  102  may recognize voice input with other forms of or other placement (in the text string) of suitable natural language expressions. 
     In some embodiments, device  102  may be responsive to command voice input, such as “Play Game of Thrones”, and in some embodiments, device  102  may be responsive to non-command voice input, such as “Thank you for smoking!”. 
     In some embodiments, device  102  is a stand-alone device and in some embodiments, device  102  is integrated or incorporated into a host device or system. In nonlimiting examples, device  102  may be a part of a computer host system, a smartphone host, or a tablet host. 
     Device  102  may receive voice input  118  or  120  by wire or wireless transmission. In a wireless transmission example, as shown in  FIG. 1 , device  102  receives voice input  118  and  120  via transmissions  122  and  124 , respectively. As previously noted, device  102  may receive input  118  or  120  at receiver  116 . In some embodiments, receiver  116  may be a microphone communicatively coupled to device  102  through wire or wireless communication coupling. In some embodiments, receiver  116  is integral to device  102 , as shown in  FIG. 1 , and in some embodiments, receiver  116  resides externally to device  102 . 
     Device  102  may be incorporated into a communication network. For example, device  102  may be part of a private or public cloud network system, housed in a network element, such as a network server. In some embodiments, device  102  is communicatively coupled to classifier binary model  104  through a communication network, the communication network may receive queries from device  102  and transmit the received queries to classifier binary model  104 . In a direct communication coupling embodiment between device  102  and classifier binary model  104 , as shown in  FIG. 1 , classifier binary model  104  and device  102  may communicate through wire or wirelessly. In some embodiments, binary model  104  is integrated into device  102  and in some communication network-based embodiments, binary model  104  may be a part of a network element in the communication network. 
     Content database  106  may be made of one or more database instances directly or indirectly communicatively coupled to one another. In some embodiments, content database  106  is a SQL-based (relational) database and in some embodiments, content database  106  is a NoSQL-based, (non-relational) database. 
     In some embodiments, classifier binary model  104  implements binary classification techniques to assist with NLU pre-processing operations and modeling to achieve a simple, plug-and-play and cost-effective NLU system architecture. For example, classifier binary model  104  assists in implementing a reduced training set to facilitate minimal NLU system architecture change and promote plug-and-play modularity. In some embodiments, classifier binary model  104  may be a binary classifier (also known as a “binomial classifier”) predicting between two groups (or classifications) on the basis of a classification rule. The classifier binary models of example embodiments shown in  FIGS. 1-4 , may discriminate between two groups of queries. By way of example, binary model  104  of  FIG. 1  may implement a query group classification based on a query classification rule with queries that include an obsequious expression and another query group classification with queries that do not include an obsequious expression. In another example, binary model  104 , in accordance with an action classification rule, may classify queries into a query group with prescribed actions to be performed and a query group with prescribed actions not to be performed. 
     In some embodiments, classifier binary model  104  is trained with an N-number of queries, “N” being an integer value. For example, classifier binary model  104  may be trained with N number of a combination of command queries, and non-command queries. Generally, the greater the number of training queries, N, the more reliably the classification may be applied during operation of system  100 . 
     With continued reference to  FIG. 1 , an example natural language model training and operation is now described relative to a natural language model training process  500  of  FIG. 5 .  FIG. 5  illustrates a flow chart of a natural language model training process, in accordance with some embodiments and methods. In  FIG. 5 , the natural language model training process  500  is disclosed in accordance with some embodiments and methods. In process  500 , at step  502 , binary model  104  receives a text string, such as text string  132  or text string  134 , from device  102 , as previously described. The received text string includes at least one content entity. For example, text string  132  includes content entity “Thank you for smoking” and text string  134  includes content entity “Play Game of Thrones”. 
     Next, at step  504  in  FIG. 5 , binary model  104  performs a determination of whether the text string of step  502  includes an obsequious expression. For example, binary model  104  may determine that “Thank you for smoking” includes the obsequious expression “thank you” or “Play Game of Thrones, please” includes the obsequious expression “please”. In some embodiments, binary model  104  determines the presence or absence of an obsequious expression in a text string based on a comparison test. For example, binary model  104  may determine whether the text string includes an obsequious expression by comparing the obsequious expression to a list of stored obsequious expressions for a match. For example, “thank you” may be compared to a list of stored obsequious expressions that may or may not include “thank you” and “please” may be compared to the same or a different list of stored obsequious expressions that may or may not include “please” and that may or may not include “thank you”. The list of stored obsequious expressions may be stored in database  106  or in a different database or a combination of database  106  and one or more other databases. The list of obsequious expressions may be stored in a storage device other than a database, such as large data storage made of nonvolatile or volatile (or a combination) memory. In some embodiments, binary model  104  may implement an obsequious expression identification operation by employing one or more other or additional suitable classification prediction algorithms. 
     At step  504 , in response to binary model  104  determining the text string includes an obsequious expression, process  500  proceeds to step  506 , otherwise, in response to binary model  104  determining the text string does not include an obsequious expression, process  500  proceeds to step  512 . At step  512 , binary model  104  forwards the query with the content entity to content database  106  for storage and maintenance. For example, binary model  104  may forward the query with the content entity to update content entity data structure  130  in database  106 . Subsequently, the query may be forwarded to an NLU processor for NLU processing. For example, binary model  104  may forward the query “Thank you for smoking!” to database  130  and update or cause updating of content entity data structure  130  with the content identity of step  502  for NLU processing by an NLU processor  1014 , in  FIG. 10 . At step  512 , the query includes the text string of step  502  with no part excluded, whereas, at step  508 , the query is stripped of the obsequious expression part of the text string to facilitate legacy system architecture integration, for example to plug into a system with NLU processing devices, such as NLU processor  914 , with little to no architectural change. 
     In some embodiments, content database  106  houses and manages obsequious expressions analogously with content entities. That is, as obsequious expressions are identified by binary model  104 , content database  106  may update (or caused to be updated) an obsequious expression data structure with the identified obsequious expressions. Alternatively, or additionally, the obsequious expressions of the obsequious expression data structure may subsequently be part of or make up the entire training set for predicting obsequious expressions to improve obsequious expression distinction prediction, for example, whether an obsequious expression is intended as an obsequious expression, or not. Employing an obsequious expression prediction model may improve the decision-making capability of process  500  (or processes  600 - 800 ) by further assisting with overall natural language predictions of the NLU system. In some embodiments, obsequious expression data structures may reside in a content database other than content database  106  or span across multiple content databases. 
     Next, at step  506  of process  500 , binary model  104  determines whether the obsequious expression detected at step  504  describes the content entity. For example, binary model  104  may determine whether the obsequious expression “thank you” of text string  132  or the obsequious expression “please” of text strings  134  describes a corresponding content entity. For text string  132 , binary model  104  may determine the obsequious expression “thank you” describes “for smoking” (not intended as an obsequious expression) and for text string  134 , binary model  104  may determine the obsequious expression “please” does not describe “play Game of Thrones” (intended as an obsequious expression). In some embodiments, binary model  104  facilitates the foregoing obsequious expression descriptor identification, at step  506 , by implementing NLU algorithms, such as, without limitation, as discussed above. In some embodiments, binary model  104  performs the determination step  506  by implementing a suitable natural language understanding algorithm for reliable obsequious expression description detection. 
     In response to determining the obsequious expression describes the corresponding content entity at step  506 , process  500  proceeds to step  510 , otherwise, in response to determining the obsequious expression does not describe the corresponding content entity at step  506 , process  500  proceeds to step  508 . 
     At step  508 , binary model  104  forwards the query with the content entity but without the obsequious expression to content database  106  for subsequent NLU processing as discussed relative to step  512  above. Taking the text string  134 , “Play Game of Thrones, Please!”, as an example, binary model  104  forwards “play Game of Thrones” but not “please” to content entity data structure  130  of content database  106 . Accordingly, no model re-training is necessary. 
     At step  510 , binary model  104  forwards the query with the content entity including the corresponding obsequious expression to content database  106  for subsequent NLU processing as discussed relative to step  512  above. Taking the text string “Thank you for smoking!” example, binary model  104  forwards the entire string “thank you for smoking” to a corresponding content entity data structure in database  106 . 
     In example embodiments, queries generated at steps  512 ,  508 , and  510  are employed by an NLU processor, such as NLU processor  1014  of  FIG. 10 , for further natural language recognition processing. 
     Although a particular order and flow of steps is depicted in each of  FIGS. 8-10 , it will be understood that in some embodiments one or more of the steps may be modified, moved, removed, or added, and that the flows depicted in  FIGS. 8-10  may be suitably modified. 
       FIG. 2  illustrates a natural language understanding (NLU) system, in accordance with various disclosed embodiments and methods. In  FIG. 2 , a natural language understanding (NLU) system is configured as a natural language understanding (NLU) system  200 , in accordance with various disclosed embodiments and methods. In some embodiments, NLU system  200  is configured analogously to NLU system  100  with exceptions as described and shown relative to  FIG. 2 . In  FIG. 2 , NLU system  200  is shown to include a device  202 , a classifier binary model  204 , and a content database  206 , in accordance with disclosed embodiments. Database  206  is analogous to database  106  but functions performed by binary model  204  deviate from those of binary model  104  as described below. 
     In some embodiments, system  200  implements a query generation method using a trained natural language model in accordance with the steps of process  600 . Device  202  receives voice input  218  or  220  by wire or wirelessly, via transmission  222  and  224 , respectively, and transcribes or has transcribed voice input  218  or  220  to text string  234  or text string  232 , respectively. At step  602 , device  202  may receive input  218  or  220  at receiver  216 . In some embodiments, receiver  216  may be implemented as a microphone communicatively coupled to device  202  through wire or wirelessly, as discussed relative to the receiver  116  of  FIG. 1 . 
     Next, at step  604 , binary model  204  performs a determination as to whether the text string of step  602  includes an obsequious expression. As discussed, relative to step  504  of  FIG. 5 , in some embodiments, binary model  204  may make an obsequious expression identification determination at step  604  in various manners. For example, binary model  204  may determine the presence or absence of an obsequious expression based on a comparison test, as earlier described, or in accordance with other suitable techniques. 
     In response to determining the text string includes an obsequious expression at step  604 , process  600  proceeds to step  608 , otherwise, if at step  604 , binary model  204  determines the text string of step  602  does not include an obsequious expression, process  600  proceeds to step  606 . With continued reference to the example embodiment of  FIG. 2 , in response to binary model  204  determining text string  232  or text string  234  of voice input  220  or voice input  218 , respectively, includes an obsequious expression, binary model  204  implements step  608  of process  600  and in response to binary model  204  determining text string  232  or text string  234  does not include an obsequious expression, binary model  204  implements step  606  of process  600 . 
     At step  606 , a query is generated for natural language voice-recognition processing (or NLU processor  914 ) that includes the entirety of the text string of step  602 . In an example application with reference to  FIG. 2 , assuming device  202  receives voice input  220  through transmission  224 , device  202  forwards the text string “play Game of Thrones”  232 , fully intact, to binary model  204  and binary model  204  performs an obsequious expression determination (at step  604  in  FIG. 6 ) that yields no obsequious expression is found in the text string “Play Game of Thrones”. Accordingly, binary model  204  includes the entirety of the text string in the query and database  206  is updated similarly to the database  106  updating explained above. That is, a content entity data structure  230  of database  206  is updated in accordance with the manner described above relative to content entity data structure  130 . 
     But in response to binary model  204  determining the text string of step  602  includes an obsequious expression, binary model  204  tests the obsequious expression at step  608 , as discussed with reference to step  506  of  FIG. 5 . Binary model  204  may determine the obsequious expression to describe the content entity, therefore, the obsequious expression is an unintended polite expression. In some embodiments, binary model  204  may perform step  608  by implementing a natural language recognition algorithm, such as the list presented with reference to step  506  of  FIG. 5 . In response to determining the obsequious expression describes the content entity at step  608 , process  600  proceeds to step  608  and in response to determining the obsequious expression does not describe the content entity at step  608 , process  600  proceeds to step  612 . At step  610 , the query is generated with the content entity and the obsequious expression and at step  612 , the query is generated with the content entity but without the obsequious expression. 
     In response to generating the query at steps  606 ,  610  and  612 , binary model  204  updates the content entity data structure  230  of database  206  and transmits the generated query to the natural language model to train the natural language model with the query. For example, the query may be transmitted to NLU processor  1014  of  FIG. 10 . 
     In some embodiments, device  202  may control operational features of a media device, such as a media device  228 . For example, device  202  may control power-on, power-off and play mode operations of media device  228 . In these embodiments, device  202  may control the operation of media device  228  in accordance with binary model  204  prediction outcomes. For example, at step  608  in process  600 , in response to the binary model  204  prediction being that the obsequious expression does not describe the corresponding content entity, device  204  may respond positively to a command query. In a practical operation, taking text string  234  as an example, if binary model  204  decides that the obsequious expression “please” does not describe “play Game of Thrones”, device  204  may communicatively cause media device  228  to play Game of Thrones because at the earlier  604  step, binary model  204  determined that an obsequious expression is present in text string  234 . In an additional practical example, assuming process  600  makes it to step  606 , where binary model  204  decides that the obsequious expression “thank you” in absent in text string  230  (“Play Game of Thrones!”), device  204  may not consummate a play operation on media device  228  consistent with the command query in the text string  230  to play Game of Thrones. 
     In some embodiments, media device  228  may be a device capable of playing media content as directed by device  204 . For example, media device  228  may be a smart television, a smartphone, a laptop or other suitable smart media content devices. 
       FIG. 3  illustrates a natural language understanding (NLU) system, in accordance with various disclosed embodiments and methods. In  FIG. 3 , a natural language understanding (NLU) system is configured as a natural language understanding (NLU) system  300 , in accordance with various disclosed embodiments and methods. In some embodiments, NLU system  300  is configured analogously to NLU systems  100  and  200  with exceptions as described and shown relative to  FIG. 3 . In  FIG. 3 , NLU system  300  is shown to include a device  302 , a classifier binary model  304 , and a content database  306 , in accordance with disclosed embodiments. Database  306  is analogous to databases  106  and  206  but functions performed by binary model  304  deviate from those of binary models  104  and  204  as described below. 
     In some embodiments, system  300  implements an action of a query using a trained natural language model of an NLU system in accordance with some of the steps of process  700  ( FIG. 7 ) and process  800  ( FIG. 8 ). Device  302  receives voice input  318  or  320  by wire or wirelessly, via transmission  322  and  324 , respectively. A natural language model training pre-processing unit  350  may include device  302 , binary model  204  and content database  306  or a combination thereof, as described relative to pre-processing unit  150  of  FIG. 1 . In accordance with an example operation, pre-processing unit  350  performs an action of a query based on a text string of the query corresponding to a prescribed action. The query includes at least a content entity with the text string. For example, device  302  may receive voice input  318  or  320  and in response, device  302  may transcribe or have transcribed the received voice input to a text string in manners described above, for example. 
     Pre-processing unit  350  may determine whether the text string corresponds to an audio input of a classified group (a user type). In some embodiments, group classification may be based on various characteristics or attributes such as, without limitation, age (adults versus children), gender, demographics, as previously discussed. For example, a group may be classified based on one or more acoustic characteristics of audio signals corresponding to the voice (or audio) input  320  and  318  ( FIG. 3 ). In some embodiments, the acoustic characteristics of a voice input may determine the classified group. For example, certain spectral characteristics of voice input  318  or  320  may determine a group at  332  ( FIG. 3 ) or at step  704  ( FIG. 7 ) based on a group classification. In some embodiments, a group is determined based on acoustic characteristics or other suitable voice processing techniques, such as those disclosed in Patent Cooperation Treaty (PCT) Application No. PCT/US20/20206, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al., incorporated herein by reference as though set forth in full and Patent Cooperation Treaty (PCT) Application No. PCT/US20/20219, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al., incorporated herein by reference as though set forth in full. In some embodiments, the audio input user type at  322  and/or step  702  may be implemented using other suitable spectral analysis techniques. 
     With continued reference to  FIG. 3 , in response to determining the text string corresponds to an audio input from a child, pre-processing unit  350  may determine whether the text string includes an obsequious expression. Based on the outcome of the determination, pre-processing unit  350  determines whether the text string includes an obsequious expression, or not, and decides to perform the prescribed action, or not. For example, in response to determining the text string includes an obsequious expression, pre-processing unit  350  may determine to perform the prescribed action and in response to determining the text string does not include the obsequious expression, pre-processing unit  350  may determine to not perform the prescribed action. 
     As with the embodiments of  FIGS. 1 and 2 , the functions of device  302 , binary model  304  or a combination thereof may be performed partly or entirely in a communication network by a communication network element. 
     Device  302  may receive voice input  318  or voice input  320  at receiver  316 . In some embodiments, receiver  316  may be implemented as a microphone communicatively coupled to device  302  through wire or wirelessly, as discussed relative to the receiver  116  of  FIG. 1 . 
     In some embodiments, device  302  receives voice input  318  or voice input  320  and transcribes or has transcribed the received voice input to a text string. For example, device  302  may transcribe voice input  318  to text string “show me Barney, please” or voice input  320  to text string “show me Barney”. Device  302  transmits a query with the transcribed text string to binary model  304 . The query includes a content entity with the text string. Stated differently, the text string, or parts thereof, is a categorized entity of the content entities of content database  306 . In the example of  FIG. 3 , the text string corresponding to voice input  318  or voice input  320  corresponds to a prescribed action, e.g., to play (or show) a show on a media device. Device  302  may direct a media device, such as media device  328 , to perform the prescribed action. For example, device  302  may direct media device  328  to power-on or power-off. In response to a text string corresponding to voice input  318  or voice input  320 , device  302  may solicit a play action from media device  328  causing media device  328  to play the show Barney, for example. But performing the prescribed action is qualified in some embodiments. In the embodiment of  FIGS. 3 and 4 , performing the prescribed action hinges on detecting a child&#39;s voice, at  332  in  FIG. 3 , whether the text string includes an obsequious expression, at  334 , and whether the obsequious expression is intended as an obsequious expression or rather describes or corresponds to a remaining portion of the text string, i.e., the non-obsequious expression portion of the text string. In some embodiments, if binary model  304  does not detect a child&#39;s voice, the prescribed action is not performed by device  302  and if binary model  304  detects a child&#39;s voice, binary model  302  tests the text string of the received query for the presence or absence of an obsequious expression, at  334 . In response to detecting an obsequious expression at  334 , binary model  304  causes device  302  to play Barney. For example, assuming device  302  receives voice input  318  from a child at receiver  316 , device  302  transmits a query with text string “show me Barney, please” to binary model  304 . Binary model  304  determines the text string to originate from a child at  332  and tests the text string for including a polite expression at  334 . In this example, because the text string includes the term “please”, binary model  304  determines the prescribed action of playing Barney should be performed and directs device  302  to cause media device  328  to play Barney. On the other hand, in response to voice input  320 , binary model  304  while determining the voice input  320  originates from a child at  332 , at  334 , device model  304  detects the absence of a polite expression and does not enable device  302  to cause media device  328  to play Barney. The prescribed action need not be a play action, it can be a power-on or other types of actions controllable by a device determinative of a child&#39;s voice and obsequious expressions. In some embodiments, binary model  304  or other suitable devices may cause media device  328  to perform the action. In some embodiments, the action is not performed until the detected obsequious expression of the text string is tested for describing the text string as described relative to steps  506  and  608  of  FIGS. 5 and 6 , respectively. 
     Referring now to  FIGS. 3 and 7 , at step  702  of process  700 , binary model  304  receives a query from device  302  that includes at least a content entity with a text string corresponding to a prescribed action. The prescribed action is based on a corresponding voice input, as described above. For example, the prescribed action of both voice input  318  and  320  is “show me Barney”. Device  302  transmits the text string corresponding to voice input  318  or  320  to binary model  304  for classification. Binary model  304  performs steps  704 ,  706 ,  708 , and the steps of process  800  ( FIG. 8 ) to determine whether to perform the action prescribed by the query that is forwarded by device  302 . 
     More specifically, at step  704 , binary model  304  performs a determination of whether the text string of step  702  corresponds to an audio input from a child. In some embodiments, binary model  304  makes the determination based on spectral analysis. Nonlimiting example spectral analysis techniques or other suitable voice recognition techniques are disclosed in Patent Cooperation Treaty (PCT) Application No. PCT/US20/20206, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al. and Patent Cooperation Treaty (PCT) Application No. PCT/US20/20219, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al. In some embodiments, binary model  304  tests for a child&#39;s voice by implementing other suitable child voice detection techniques. In response to binary model  304  detecting a child&#39;s voice at step  704 , process  700  proceeds to step  706 , otherwise, in response to binary model  304  detecting the absence of a child&#39;s voice at step  704 , process  700  proceeds to step  802  of process  800  ( FIG. 8 ). 
     At step  706 , binary model  304  determines whether the text string corresponding to voice input  318  or  320  includes an obsequious expression. As earlier noted, relative to steps  504  and  604  of  FIGS. 5 and 6 , respectively, in some embodiments, binary model  304  detects the presence or absence of an obsequious expression by implementing a comparison test but binary model  304  may employ other suitable algorithms for the determination of step  706 . If at step  706 , binary model  304  detects an obsequious expression, process  700  proceeds to step  714 , otherwise, if at step  706 , binary model  304  detects the absence of an obsequious expression, process  700  proceeds to step  708 . 
     At step  714 , binary model  304  determines to perform the prescribed action in the query forwarded by device  302 . For example, assuming voice input  318  from a child is received by device  302 , binary model  304  detects the child&#39;s voice, determines “please” is in the text string that corresponds to the received voice input and it is an intended obsequious expression. Accordingly, binary model  304  may direct device  302  to cause media device  328  to play Barney. On the other hand, at step  708 , given the same example scenario, an opposite determination is reached and binary model  304  does not direct device  302  to enable media device  328  to play Barney. 
     At step  802  of process  800  ( FIG. 8 ), binary model  304  determines whether the text string corresponding to voice input  318  or voice input  320  includes an obsequious expression. In response to determining the text string includes an obsequious expression at step  802 , binary model  304  performs step  806 , otherwise, in response to determining the text string does not include an obsequious expression, binary model  304  performs step  804 . At step  804 , the prescribed action of the forwarded query is determined not to be performed whereas at step  806 , a further determination is performed as to whether the detected obsequious expression of step  802  is an intended polite term or whether it describes, relates to corresponds to a non-obsequious expression. For example, a child voice input “thank you for playing Barney” would not cause the prescribed action to be performed by “thank you” while detected as an obsequious expression at step  802 , would be determined to be an unintended polite term. Accordingly, in response to a determination at step  806  that the detected obsequious expression is an unintended polite term, binary model  304  performs step  808  whereas in response to a determination at step  806  that the detected obsequious expression is an intended polite term, binary model  304  performs step  810  and determines that the prescribed action is to be performed. 
     At step  708  of process  700 , binary model  304  determines not to perform the prescribed action because, assuming voice input  320  from a child is received by device  302 , the corresponding text string does not contain a polite term. Accordingly, media play  328  does not play Barney. In some embodiments, the binary model may take further action, as discussed relative to the embodiment of  FIG. 4 . 
       FIG. 4  illustrates a natural language understanding (NLU) system, in accordance with various disclosed embodiments and methods. In  FIG. 4 , a natural language understanding (NLU) system is configured as a natural language understanding (NLU) system  400 , in accordance with various disclosed embodiments and methods. In some embodiments, NLU system  400  is configured analogously to NLU systems  100 - 300  with exceptions as described and shown relative to  FIG. 4 . In  FIG. 4 , NLU system  400  is shown to include a device  402 , a classifier binary model  404 , and a content database  406 , in accordance with disclosed embodiments. Database  406  is analogous to databases  106 ,  206 , and  306  but functions performed by binary model  404  deviate from those of binary models  104 - 304  as described below. 
     In some embodiments and as earlier noted, binary model  404  of system  400  implements further actions in response to a determination that an obsequious expression is absent in a text string corresponding to voice input (or audio input) from a particular user type (or user type of interest). For example, as discussed relative to  FIGS. 3 and 7 , an audio input user type may be a child. That is, voice input  318 , in  FIG. 3 , and/or voice input  418  in  FIG. 4  may correspond to a child&#39;s voice. Assuming the originator of voice input  418  is a child, binary model  404 , in  FIG. 4 , detects a child&#39;s voice at  432 , or not, and in response to detecting a child&#39;s voice looks for an obsequious expression at  434 , similar to that which is done at steps  334  and  334  of  FIG. 3 , respectively. 
     In response to detecting the absence of a child&#39;s voice at  432 , binary model  404  determines the prescribed action should not be performed and in response to detecting a child&#39;s voice and further detecting an obsequious expression, binary model determines that the prescribed action should not be performed. But in the latter case, binary model  404  gives a chance to the child (or originator of the voice input such as voice input  418 ) to repeat the voice input, this time with a polite expression. In some embodiments, binary model  404  may send an instructional message to the child asking to repeat the voice input with a polite term. Next, binary model  404  may wait for a time period, at  436 , for a detected response, for example, voice input  420 . In response to device  402  receiving voice input  420  at receiver  416 , binary model  404  may determine to perform the prescribed action, for example, cause media device  428  to play Barney. If binary model  404  waits the time period at  436  and no received voice input including an obsequious expression, binary model  404  determines the action should not be performed. Expiration of the time period with no voice input  420  received, therefore, causes no action to be taken by media device  428 . 
     In some embodiments, binary device  404  may implement a responsive instructional message to the child through device  402  or other suitable devices communicatively compatible with binary model  404 . In embodiments where binary model  404  sends an instruction message through device  402 , device  402  requires voice generation features, such as speakers. Binary model  404  may directly communicate with the child using voice generation features. In the embodiment of  FIG. 4 , binary model  404  implements the steps discussed relative to  FIG. 3  and additionally implements steps  710  through  718 . 
     In some embodiments, binary model  404  generates an instructional message at step  710 , as discussed relative to binary model  404  actions in  FIG. 4 . Next, at step  712 , binary model  404  performs a determination of whether the instructional message transmitted during a time period, as discussed relative to  FIG. 4  above, is received. In some embodiments, binary model  404  makes this determination by waiting for receipt of a voice input, such as voice input  420 , within a time period, as discussed relative to the binary model  404  actions of  FIG. 4 . If no voice input is detected during the time period, binary model  404  determines the instructional message was not received and proceeds to step  716  of  FIG. 7 . The time period for waiting for receipt of a responsive voice input from a child is a design choice and may be predetermined time period or may be implemented by polling or other suitable techniques. 
     When or if binary model  404  reaches step  716 , a voice input, such as voice input  420 , is detected and at step  716 , binary model  404  determines whether the received voice input includes an obsequious expression. If binary model  404  determines the voice input includes an obsequious expression, binary model  404  performs step  720 , otherwise, if binary model  404  determines the voice input does not include an obsequious expression, binary model  404  performs step  718 . At step  720 , the prescribed action of the query transmitted by device  402  is not performed and at step  718 , the prescribed action is performed, as earlier discussed. 
     In some embodiments, a process for training a classifier binary model with obsequious expressions in accordance with methods of the disclosure may be implemented.  FIG. 9  depicts an illustrative process flow for training a classifier binary model with obsequious expressions in a NLU system, in accordance with some embodiments of the disclosure. In  FIG. 9 , a process  900  depicts an illustrative process for training a classifier binary model with the presence and absence of obsequious expressions, in accordance with some embodiments of the disclosure. 
     In some embodiments, a method of training a classifier binary model is generally performed by receiving a text string including at least a content entity, determining whether the text string includes an obsequious expression. In response to determining the text string includes an obsequious expression, determining whether the obsequious expression describes the content entity and training the classifier binary model based on a determination of at least one of: an absence of an obsequious expression in response to determining the obsequious expression describes the content entity; a presence of an obsequious expression in response to determining the obsequious expression describes the content entity; an absence of an obsequious expression in response to determining the obsequious expression does not describe the content entity; and a presence of an obsequious expression in response to determining the obsequious expression does not describe the content entity. These steps are described in further detail below relative to  FIG. 9 . 
     In nonlimiting examples, a classifier binary model of an NLU system may be trained by each of the systems  100 - 400  in accordance with process  900  of  FIG. 9 . In some embodiments, any suitable NLU system may implement the process  900  of  FIG. 9 . For the purpose of simplicity, system  100  is discussed below in conjunction with the steps of process  900 . 
     At step  902 , device  102  of system  100  receives a text string including at least a content entity. For example, device  102  may receive text string  118  or text string  120 . As earlier discussed with reference to  FIG. 1 , device  102  may transmit text string  134  to classifier binary model  104  and classifier binary model  104  may implement steps  904 - 914 . In some embodiments, device  102  or other suitable devices communicatively coupled to or incorporated in device  102  or pre-processing unit  150  may implement process  900 . 
     Assuming binary model  104  is performing the steps of  FIG. 9 , after step  902 , at step  904 , binary model  104  determines whether text string  118  (or text string  120 , as the case may be) includes an obsequious expression. In response to determining an obsequious expression is found in the text string of step  902 , binary model  104  makes another determination at step  906 . In some embodiments, if no obsequious expression is found at step  904 , process  900  stops. In some embodiments, if no obsequious expression is found at step  904 , further step(s) may be implemented as a part of process  900  to train binary model  104  with the absence of an obsequious expression from the text string of step  902 . In some embodiments, the determination part of step  906  to find an obsequious expression in the text string is made in a manner similar to step  504  of  FIG. 5 , as described earlier. 
     At step  906 , binary model  104  determines whether the obsequious expression (found at step  904 ) describes the content entity of step  902 . In some embodiments, the determination part of step  906  to find whether the obsequious expression describes a content entity, or not, is performed in a manner similar to step  506  of  FIG. 5 , as discussed earlier. At step  908 , binary model  104  is trained based on the determination at step  906 . That is, at step  910 , in response to determining whether the obsequious expression describes the content entity of step  906 , in accordance with process  900 , binary model  104  is trained with at least one of the following: 1) the absence of an obsequious expression in response to determining the obsequious expression describes the content entity; 2) the presence of an obsequious expression in response to determining the obsequious expression describes the content entity; 3) the absence of an obsequious expression in response to determining the obsequious expression does not describe the content entity; and 4) the presence of an obsequious expression in response to determining the obsequious expression does not describe the content entity. 
     In the example of  FIG. 9 , assuming text string  132 , “thank you for smoking”, is received at step  902 , binary model  104  is trained at step  908  with 2) at step  910 —the presence of an obsequious expression in response to the obsequious expression describing the content entity of the text string. Now suppose, text string  134 , “play Game of Thrones, please”, is received at step  902 , binary model  104  is trained at step  908  with 4) at step  910 —the presence of an obsequious expression in response to the obsequious expression not describing the content entity. 
     In some embodiments, binary model  104  updates content database  106  based on the training and prediction determinations of steps  904  through  910 . For example, binary model  104  may update content database  106  with “please” as an obsequious expression feature that does not describe a content entity. 
     In some embodiments, obsequious expressions predictions are maintained by one or more databases or storage devices, other than content database  106 . In embodiments employing database  106  or other storage or database devices, database  106  or other storage and/or databases may maintain and update an obsequious expression content entity as discussed herein. 
     In some embodiments, parts of systems  100 ,  200 ,  300 , and  400  may be incorporated in a natural language recognition system.  FIG. 10  is an illustrative block diagram showing a natural language recognition system, in accordance with some embodiment of the disclosure. In  FIG. 10 , a natural language recognition system is configured as a natural language recognition system  1000 . Natural language recognition system  1000  includes an automatic speech recognition (ASR) transcription system  1002 , group predictor  1012  (or group classifier), natural language understanding (NLU) processor  1014 , and binary model  1004 , in accordance with some embodiments of the disclosure. In some embodiments, group predictor  1012  predicts group classification based on acoustic features and characteristics. For example, predictor  1012  can classify voice input, such as those described and shown herein, based on a group feature, such as a child voice versus an adult voice or a male voice versus a female voice. Other acoustic-based classifications are anticipated. In some embodiments, predictor  1012  employs spectral analysis techniques or other suitable voice recognition techniques to predict group classification as disclosed in Patent Cooperation Treaty (PCT) Application No. PCT/US20/20206, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al. and Patent Cooperation Treaty (PCT) Application No. PCT/US20/20219, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al. 
     Classifier binary model  1004  may be configured as binary model  104 ,  204 ,  304  or  404  in some embodiments. Binary model  1004  may include a query obsequious expression predictor  106 , a query natural language predictor  1008  and an instructional message generator  1010 . In some embodiments, one of more of the components shown in system  1000  may be implemented in hardware or software. For example, functions of one or more components may be performed by a processor executing program code to carry out the processes disclosed herein. In some embodiments, process circuitry  1140  or process circuitry  1126  may carry out the processes by executing program code stored in storage  1138  or storage  1124  of  FIG. 11 , respectively. 
     In some embodiments, query obsequious expression predictor  1006  may perform determinations at steps  504 ,  604 ,  706 ,  716 , and  802 ; natural language predictor  1008  may perform steps  506 ,  608 ,  806 ; and instructional message generator  1010  may implement transmitting an instruction message, as discussed relative to  FIG. 4 , in response to a determination of the absence of an obsequious expression assuming the corresponding text string is from a child. 
     With continued reference to  FIG. 10 , during operation, an audio signal  1016  is received by system  1002  and predictor  1012 . Audio signal  1016  may comprise more than one audio signal and in some embodiments audio signal  1016  represents a user utterance, such as a voice input, examples of which are voice inputs of  FIGS. 1-4 . System  1002  may implement speech-to-text transcription services. In some embodiments, system  1002  transcribes audio signal  1016 . In some embodiments, system  1002  performs transcription services as those described performed by devices of  FIGS. 1-4 . 
     Predictor  1012  implements child voice prediction detection, such as described relative to steps  506 ,  608 ,  706 , and  806 . In some embodiments, predictor  1012  implements child speech detection prediction as described in relation to natural language processing (NLP) by implementing voice processing techniques such as those disclosed in Patent Cooperation Treaty (PCT) Application No. PCT/US20/20206, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al. and Patent Cooperation Treaty (PCT) Application No. PCT/US20/20219, filed on Feb. 27, 2020, entitled “System and Methods for Leveraging Acoustic Information of Voice Queries”, by Bonfield et al. 
     NLU processor  1014  interacts with binary model  1004  to receive generated queries as described relative to preceding figures, receive determinative outcomes, such as to perform a prescribed action, other suitable functions, or a combination. In some embodiments, NLU processor  1014  may perform natural language recognition functions such as sentence analysis, interpretation determination, template matching, or a combination. 
       FIG. 11  is an illustrative block diagram showing an NLU system incorporating query generation and model training features, in accordance with some embodiments of the disclosure. In  FIG. 11 , an NLU system is configured as an NLU system  1100  in accordance with some embodiments of the disclosure. In an embodiment, one or more parts of or the entirety of system  1100  may be configured as a system implementing various features, processes, and displays of  FIGS. 1-10 . Although  FIG. 11  shows a certain number of components, in various examples, system  1100  may include fewer than the illustrated number of components and/or multiples of one or more of the illustrated number of components. 
     System  1100  is shown to include a computing device  1118 , a server  1102  and a communication network  1114 . It is understood that while a single instance of a component may be shown and described relative to  FIG. 11 , additional instances of the component may be employed. For example, server  1102  may include, or may be incorporated in, more than one server. Similarly, communication network  1114  may include, or may be incorporated in, more than one communication network. Server  1102  is shown communicatively coupled to computing device  1118  through communication network  1114 . While not shown in  FIG. 11 , server  1102  may be directly communicatively coupled to computing device  1118 , for example, in a system absent or bypassing communication network  1114 . 
     Communication network  1114  may comprise one or more network systems, such as, without limitation, an Internet, LAN, WIFI or other network systems suitable for audio processing applications. In some embodiments, system  1100  excludes server  1102  and functionality that would otherwise be implemented by server  1102  is instead implemented by other components of system  1100 , such as one or more components of communication network  1114 . In still other embodiments, server  1102  works in conjunction with one or more components of communication network  1114  to implement certain functionality described herein in a distributed or cooperative manner. Similarly, in some embodiments, system  1100  excludes computing device  1118  and functionality that would otherwise be implemented by computing device  1118  is instead implemented by other components of system  1100 , such as one or more components of communication network  1114  or server  1102  or a combination. In still other embodiments, computing device  1118  works in conjunction with one or more components of communication network  1114  or server  1102  to implement certain functionality described herein in a distributed or cooperative manner. 
     Computing device  1118  includes control circuitry  1128 , display  1134  and input circuitry  1102 . Control circuitry  1128  in turn includes transceiver circuitry  1162 , storage  1138  and processing circuitry  1140 . In some embodiments, computing device  1118  or control circuitry  1128  may be configured as media devices  402 ,  502 ,  600 , or  712  of  FIGS. 4, 5, 6, and 7 , respectively. In some embodiments, display  1034  is optional. 
     Server  1102  includes control circuitry  1120  and storage  1124 . Each of storages  1124 , and  1138  may be an electronic storage device. As referred to herein, the phrase “user equipment device,” “user equipment,” “user device,” “electronic device,” “electronic equipment,” “media equipment device,” or “media device” should be understood to mean any device for processing the text string described above or accessing content, such as, without limitation, wearable devices with projected image reflection capability, such as a head-mounted display (HMD) (e.g., optical head-mounted display (OHMD)), electronic devices with computer vision features, such as augmented reality (AR), virtual reality (VR), extended reality (XR), or mixed reality (MR), portable hub computing packs, a television, a Smart TV, a set-top box, an integrated receiver decoder (IRD) for handling satellite television, a digital storage device, a digital media receiver (DMR), a digital media adapter (DMA), a streaming media device, a DVD player, a DVD recorder, a connected DVD, a local media server, a BLU-RAY player, a BLU-RAY recorder, a personal computer (PC), a laptop computer, a tablet computer, a WebTV box, a personal computer television (PC/TV), a PC media server, a PC media center, a hand-held computer, a stationary telephone, a personal digital assistant (PDA), a mobile telephone, a portable video player, a portable music player, a portable gaming machine, a smartphone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same. In some embodiments, the user equipment device may have a front facing screen and a rear facing screen, multiple front screens, or multiple angled screens. In some embodiments, the user equipment device may have a front facing camera and/or a rear facing camera. On these user equipment devices, users may be able to navigate among and locate the same content available through a television. Consequently, a user interface in accordance with the present disclosure may be available on these devices, as well. The user interface may be for content available only through a television, for content available only through one or more of other types of user equipment devices, or for content available both through a television and one or more of the other types of user equipment devices. The user interfaces described herein may be provided as online applications (i.e., provided on a website), or as stand-alone applications or clients on user equipment devices. Various devices and platforms that may implement the present disclosure are described in more detail below. 
     Each storage  1124 ,  1138  may be used to store various types of content, metadata, and or other types of data. Non-volatile memory may also be used (e.g., to launch a boot-up routine and other instructions). Cloud-based storage may be used to supplement storages  1124 ,  1138  or instead of storages  1124 ,  1138 . In some embodiments, control circuitry  1120  and/or  1128  executes instructions for an application stored in memory (e.g., storage  1124  and/or storage  1138 ). Specifically, control circuitry  1120  and/or  1128  may be instructed by the application to perform the functions discussed herein. In some implementations, any action performed by control circuitry  1120  and/or  1128  may be based on instructions received from the application. For example, the application may be implemented as software or a set of executable instructions that may be stored in storage  1124  and/or  1138  and executed by control circuitry  1120  and/or  1028 . In some embodiments, the application may be a client/server application where only a client application resides on computing device  1118 , and a server application resides on server  1102 . 
     The application may be implemented using any suitable architecture. For example, it may be a stand-alone application wholly implemented on computing device  1118 . In such an approach, instructions for the application are stored locally (e.g., in storage  1138 ), and data for use by the application is downloaded on a periodic basis (e.g., from an out-of-band feed, from an Internet resource, or using another suitable approach). Control circuitry  1128  may retrieve instructions for the application from storage  1138  and process the instructions to perform the functionality described herein. Based on the processed instructions, control circuitry  1128  may, for example, perform processes  500 - 900  in response to input received from input circuitry  1102  or from communication network  1114 . For example, in response to receiving a query and/or voice input and/or text string, control circuitry  1128  may perform the steps of processes  500 - 900  or processes relative to various embodiments, such as the example of  FIGS. 1-4 . 
     In client/server-based embodiments, control circuitry  1128  may include communication circuitry suitable for communicating with an application server (e.g., server  1102 ) or other networks or servers. The instructions for carrying out the functionality described herein may be stored on the application server. Communication circuitry may include a cable modem, an Ethernet card, or a wireless modem for communication with other equipment, or any other suitable communication circuitry. Such communication may involve the Internet or any other suitable communication networks or paths (e.g., communication network  1114 ). In another example of a client/server-based application, control circuitry  1128  runs a web browser that interprets web pages provided by a remote server (e.g., server  1102 ). For example, the remote server may store the instructions for the application in a storage device. The remote server may process the stored instructions using circuitry (e.g., control circuitry  1128 ) and/or generate displays. Computing device  1118  may receive the displays generated by the remote server and may display the content of the displays locally via display  1134 . This way, the processing of the instructions is performed remotely (e.g., by server  1102 ) while the resulting displays, such as the display windows described elsewhere herein, are provided locally on computing device  1118 . Computing device  1118  may receive inputs from the user via input circuitry  1102  and transmit those inputs to the remote server for processing and generating the corresponding displays. Alternatively, computing device  1118  may receive inputs from the user via input circuitry  1102  and process and display the received inputs locally, by control circuitry  1128  and display  1134 , respectively. 
     Server  1102  and computing device  1118  may transmit and receive content and data such as media content via communication network  1114 . For example, server  1102  may be a media content provider and computing device  1118  may be a smart television configured to download media content, such as a Harry Potter episode, from server  1102 . In some embodiments implementing computing device  1118  as a smart television, the smart television may media devices  328  or  428 . Control circuitry  1120 ,  1128  may send and receive commands, requests, and other suitable data through communication network  1114  using transceiver circuitry  1160 ,  1162 , respectively. Control circuitry  1120 ,  1128  may communicate directly with each other using transceiver circuitry  1160 ,  1162 , respectively, avoiding communication network  1114 . 
     It is understood that computing device  1018  is not limited to the embodiments and methods shown and described herein. In nonlimiting examples, computing device  1018  may be any device for processing the text string described herein or accessing content, such as, without limitation, wearable devices with projected image reflection capability, such as a head-mounted display (HMD) (e.g., optical head-mounted display (OHMD)), electronic devices with computer vision features, such as augmented reality (AR), virtual reality (VR), extended reality (XR), or mixed reality (MR), portable hub computing packs, a television, a Smart TV, a set-top box, an integrated receiver decoder (IRD) for handling satellite television, a digital storage device, a digital media receiver (DMR), a digital media adapter (DMA), a streaming media device, a DVD player, a DVD recorder, a connected DVD, a local media server, a BLU-RAY player, a BLU-RAY recorder, a personal computer (PC), a laptop computer, a tablet computer, a WebTV box, a personal computer television (PC/TV), a PC media server, a PC media center, a handheld computer, a stationary telephone, a personal digital assistant (PDA), a mobile telephone, a portable video player, a portable music player, a portable gaming machine, a smartphone, or any other device, computing equipment, or wireless device, and/or combination of the same capable of suitably operating a media content. 
     Control circuitry  1120  and/or  1118  may be based on any suitable processing circuitry such as processing circuitry  1126  and/or  1140 , respectively. As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., and may include a multi-core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores). In some embodiments, processing circuitry may be distributed across multiple separate processors, for example, multiple of the same type of processors (e.g., two Intel Core i9 processors) or multiple different processors (e.g., an Intel Core i7 processor and an Intel Core i9 processor). In some embodiments, control circuitry  1120  and/or control circuitry  1118  are configured to implement an NLU system, such as systems, or parts thereof, that perform various query determination, query generation, and model training and operation processes described and shown in connection with  FIGS. 1-9 . 
     Computing device  1118  receives a user input  1104  at input circuitry  1102 . For example, computing device  1118  may receive a text string, as previously discussed. In some embodiments, computing device  1118  is a media device (or player) configured as media devices  102 ,  104 ,  202 ,  204 ,  302 ,  304 ,  402 , or  404 , with the capability to receive voice, text, or a combination thereof. It is understood that computing device  1018  is not limited to the embodiments and methods shown and described herein. In nonlimiting examples, computing device  1018  may be, without limitation, wearable devices with projected image reflection capability, such as a head-mounted display (HMD) (e.g., optical head-mounted display (OHMD)), electronic devices with computer vision features, such as augmented reality (AR), virtual reality (VR), extended reality (XR), or mixed reality (MR), portable hub computing packs, a television, a Smart TV, a set-top box, an integrated receiver decoder (IRD) for handling satellite television, a digital storage device, a digital media receiver (DMR), a digital media adapter (DMA), a streaming media device, a DVD player, a DVD recorder, a connected DVD, a local media server, a BLU-RAY player, a BLU-RAY recorder, a personal computer (PC), a laptop computer, a tablet computer, a WebTV box, a personal computer television (PC/TV), a PC media server, a PC media center, a handheld computer, a stationary telephone, a personal digital assistant (PDA), a mobile telephone, a portable video player, a portable music player, a portable gaming machine, a smartphone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same. 
     User input  1004  may be a voice input such as the voice input shown and described relative to  FIGS. 1-4 . In some embodiments, input circuitry  1102  may be a device, such as the devices of  FIGS. 1-4 . In some embodiments, input circuitry  1102  may be a receiver, such as the receivers of  FIGS. 1-4 . Transmission of user input  1104  to computing device  1118  may be accomplished using a wired connection, such as an audio cable, USB cable, ethernet cable or the like attached to a corresponding input port at local device  300 , or may be accomplished using a wireless connection, such as Bluetooth, WIFI, WiMAX, GSM, UTMS, CDMA, TDMA, 3G, 4G, 4G, 5G, Li-Fi, LTE, or any other suitable wireless transmission protocol. Input circuitry  304  may comprise a physical input port such as a 3.5 mm audio jack, RCA audio jack, USB port, ethernet port, or any other suitable connection for receiving audio over a wired connection, or may comprise a wireless receiver configured to receive data via Bluetooth, WIFI, WiMAX, GSM, UTMS, CDMA, TDMA, 3G, 4G, 4G, 5G, Li-Fi, LTE, or other wireless transmission protocols. 
     Processing circuitry  1140  may receive input  1104  from input circuitry  1102 . Processing circuitry  1140  may convert or translate the received user input  1104  that may be in the form of gestures or movement to digital signals. In some embodiments, input circuitry  1102  performs the translation to digital signals. In some embodiments, processing circuitry  1140  (or processing circuitry  1126 , as the case may be) carry out disclosed processes and methods. For example, processing circuitry  1140  or processing circuitry  1126  may perform processes  500 ,  600 ,  700 ,  800  and  900  of  FIGS. 5, 6, 7, 8 and 9 , respectively. 
     The systems and processes discussed above are intended to be illustrative and not limiting. One skilled in the art would appreciate that the actions of the processes discussed herein may be omitted, modified, combined, and/or rearranged, and any additional actions may be performed without departing from the scope of the invention. More generally, the above disclosure is meant to be exemplary and not limiting. Only the claims that follow are meant to set bounds as to what the present disclosure includes. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and flowcharts or examples relating to one embodiment may be combined with any other embodiment in a suitable manner, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real time. It should also be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods.