Abstract:
A novel system for automatic reading tutoring provides effective error detection and reduced false alarms combined with low processing time burdens and response times short enough to maintain a natural, engaging flow of interaction. According to one illustrative embodiment, an automatic reading tutoring method includes displaying a text output and receiving an acoustic input. The acoustic input is modeled with a domain-specific target language model specific to the text output, and with a general-domain garbage language model, both of which may be efficiently constructed as context-free grammars. The domain-specific target language model may be built dynamically or “on-the-fly” based on the currently displayed text (e.g. the story to be read by the user), while the general-domain garbage language model is shared among all different text outputs. User-perceptible tutoring feedback is provided based on the target language model and the garbage language model.

Description:
BACKGROUND 
     Automatic reading tutoring has been a growing application for natural language processing and automatic speech recognition tools. An automatic reading tutoring system can provide a story or other text for a student to read out loud, and track the student&#39;s reading and any errors the student makes. It can diagnose particular kinds of systematic errors the student makes, respond to errors by providing assistance to the student, and evaluate d student&#39;s reading aptitude. 
     Automatic reading tutoring systems typically involve building a language model for a given story or other text prior to presenting the text to the student to begin a reading tutoring episode. Building the language model for the story or other text typically involves preparing to accommodate all possible words in the text being used, as well as all possible mistaken words the student might utter, to the best that these can be foreseen. This is particularly difficult for reading tutoring systems since one of their main audiences is children learning to read their native language, and children tend not only to make many unpredictable mistakes in reading, but also to get distracted and make frequent utterances that have nothing to do with the displayed text. 
     Building the language model for the text also typically involves accessing a large corpus and requires a significant amount of time to prepare. It also presents a large processing burden during runtime, which tends to translate into processing delays between when the student reads a line and when the computer is able to respond. Such delays tend to strain the student&#39;s patience and interrupt the student&#39;s attention. Additionally, the reading tutoring system cannot flag all possible reading errors, and may erroneously indicate the student has made an error when the student reads a portion of text correctly. Trying to improve the system&#39;s ability to catch errors and not indicate false alarms typically involves raising the time spent processing and further stretching out the delays in the system&#39;s responsiveness, while trying to reduce the system&#39;s lag time in responding conversely tends to degrade performance in error detection and false alarms. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A novel system for automatic reading tutoring is disclosed herein, that naturally provides effective error detection and reduced false alarms combined with low processing time burdens and response times short enough to maintain a natural, engaging flow of interaction. According to one illustrative embodiment, an automatic reading tutoring method includes displaying a text output and receiving an acoustic input. The acoustic input is modeled with a domain-specific target language model specific to the text output, and with a general-domain garbage language model. User-perceptible feedback is provided based on the target language model and the garbage language model. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a flow diagram of a method for automatic reading tutoring, according to an illustrative embodiment. 
         FIG. 2  depicts a block diagram of a language modeling system, according to an illustrative embodiment. 
         FIG. 3  depicts a block diagram of a language modeling system, according to an illustrative embodiment. 
         FIG. 4  depicts a block diagram of a language modeling system, according to an illustrative embodiment. 
         FIG. 5  depicts a graph illustrating a feature of an automatic reading tutoring system, according to an illustrative embodiment. 
         FIG. 6  depicts a block diagram of one computing environment in which some embodiments may be practiced, according to an illustrative embodiment. 
         FIG. 7  depicts a block diagram of a mobile computing environment in which some embodiments may be practiced, according to an illustrative embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  depicts a flow diagram of an automatic reading tutoring method  100 . Automatic reading tutoring method  100  may be implemented in any of a wide variety of software and computing implementations, as will be familiar to those skilled in the art, and which are surveyed below.  FIGS. 2-4  depict block diagrams of automatic reading tutoring systems  200 ,  300 ,  400  according to various illustrative embodiments that may be associated with automatic reading tutoring method  100  of  FIG. 1 . Further details of various illustrative embodiments are provided below, which are illustrative and indicative of the broader meaning and variety associated with the disclosure and the claims provided herein. 
     Automatic reading tutoring method  100  includes step  102 , of displaying a text output, such as a sentence of a paragraph from within a larger story or other text. Method  100  next includes step  104 , of receiving an acoustic input, such as a spoken-word utterance from a student reading aloud the text output from step  102 . Method  100  further includes step  106 , of modeling the acoustic input with a domain-specific target language model specific to the text output; and step  108 , of further modeling the acoustic input with a general-domain garbage language model. 
     The target language model and the garbage language model may be used, for example, to detect elements of the acoustic model that do not correspond properly to the text output, and to identify these elements as miscues. The target language modeling and garbage language modeling are done in parallel and used together, although in an asymmetrical fashion, since the target language model is domain-specific while the garbage language model is general-domain, providing unique advantages. Because the speech inputs are compared between the parallel target language model and garbage language model, and assigned to the one or the other, the language modeling can be thought of as polarizing the speech inputs. The steps  106 ,  108  of modeling the language models are further elaborated below. 
     Method  100  also includes step  110 , of providing user-perceptible feedback based on the target language model and the garbage language model. Such user-perceptible feedback may provide confirmation and/or encouragement when the student reads the texts correctly, and may include information on any miscues, such as by providing suggestions or other assistance, when the student makes a mistake in reading the text. Such assistance may take the form of audible corrections to the miscues, provided by the computing device in spoken word, for example. These spoken-word corrections may be pre-recorded, or may be generated by a text-to-speech tool, for example. The assistance may also take the form of displaying a phonetic representation of the portion of the text output corresponding to a miscue, on a monitor or other visual output, for example. Such user-perceptible feedback thereby provides automatic reading tutoring for the student. 
     The user-perceptible feedback may also take the form of an evaluation of how well the acoustic input corresponds to the text output, such as a score representing how much of the acoustic input is free of miscues. Such an evaluation can constitute a score on which the student is graded for a test, or it can be used by the student or the student&#39;s teacher or parent to keep track of the student&#39;s progress over time and set goals for further learning. 
     In an application in which the reading tutoring system is provided for students of a second language, as opposed to children learning to read their native language, the user-perceptible feedback may include displaying a translation into a different language, such as the student&#39;s native language, of a portion of the text output for which the acoustic input includes a miscue. 
     Such user-perceptible feedback may also be provided when the system evaluates the acoustic input to correctly correspond to the text output, indicating that the student has correctly read the text output. (“The system” here, and throughout the remaining disclosure, may be used as a shorthand reference to a computing system executing an illustrative embodiment, such as method  100 .) For example, the system may provide a low-key, unobtrusive running indicator of each of the student&#39;s spoken-word inputs that represents a correct reading of the corresponding text output. This might be, for example, a green light that lights up in a corner of a screen every time the student correctly reads a text output, in one illustrative embodiment. 
     Method  100  further includes decision node  112 , in which the system evaluates whether it is finished with a larger reading tutoring episode of which the text output of step  102  is part. 
     If the system is finished with a reading tutoring episode, the method may proceed to endpoint  114 . If it is not yet finished, the system may return to the beginning and iteratively repeat the process of displaying additional text outputs as in step  102 , receiving corresponding acoustic inputs as in step  104 , assembling additional domain-specific target language models respectively based on each of the additional text outputs as in step  106 , modeling the acoustic input with the general-domain garbage language model as in step  108 , and provide new user-perceptible feedback for that iteration, as in step  110 . 
     Step  106 , of modeling the acoustic input with a domain-specific target language model specific to the text input, may be performed within a restricted period of time relative to when its respective text output is displayed as in step  102 . That is, the system may calculate a language model score for the target words of the displayed text only once the target words are called up for display, which may be in a sentence or a paragraph at a time, for example. In this illustrative embodiment, therefore, a small language model is built just for an individual sentence or paragraph at the time that short text sample is brought up for display, for the student to read. 
     This provides a number of advantages. Because the text sample is so small, the system can process it in a very short time, short enough that the student will not experience any noticeable delay. This also allows the system to begin functioning, or to begin a tutoring episode based on a text or a position within a text just selected by the student, without having to then stop and process the entire text prior to allowing the student to continue. 
     A reading tutoring system such as this is illustratively depicted as language modeling system  200  in  FIG. 2 . Language modeling system  200  involves a combination of general-domain garbage modeling and domain-specific target modeling, implemented in this illustrative embodiment with target language model  206 , a domain-specific statistical language model implemented as a Context-Free Grammar (CFG), in this embodiment; and garbage language model  210 , a general-domain N-gram statistical language model implemented as a Context-Free Grammar (CFG), in this embodiment. Target language model  206  is engaged through grammar entry node  202 , and leads to grammar exit node  204 , in this illustrative embodiment. 
     Ordinarily, a general-domain N-gram statistical language model has a low processing burden but poor modeling performance, while a domain-specific statistical language model ordinarily has high modeling performance but imposes a high processing burden involving significant delays. 
     Language modeling system  200  combines the best of both worlds, as an on-line interpolated language model, with the domain-specific target language model  206  at its core, which is trained on-the-fly from a limited set of training sentences, such as a then-current sentence or paragraph in a story or other text output. At the same time, the general-domain garbage language model  210 , which may be implemented as an N-Gram Filler, such as a restricted version of a dictation grammar, is attached to target language model  206  through a unigram back-off node  208  comprised in target language model  206 . Garbage language model  210  thereby provides robustness in that it is able to siphon off general miscues without having to have them defined in advance. The interpolation between target language model  206  and garbage language model  210  may be promoted by reserving some unigram counts for unforeseen garbage words, to get swept aside from target language model  206  by garbage language model  210 . 
     Beginning from the path from grammar entry node  202  to target language model  206 , target language model  206  then has a weight w 1  that controls the possibility of moving from within target language model  206  to unigram back-off node  208 . A second weight, w 2 , controls the possibility of moving from unigram back-off node  208  to garbage language model  210 . The target language model  206  is relatively small, such as enough to occupy a few kilobytes of memory in one embodiment, due to being based on only the text sample currently on display at a given time, such as a paragraph or a sentence. The garbage language model  210  is significantly larger—in the same embodiment, it may be enough to occupy several megabytes of memory—but this does not pose any significant processing burden or reaction time delay, because the one single garbage language model  210  may be shared for the purposes of all the text samples that are successively modeled with the target language model  206 . So, the only new language model that is built within the timeframe of providing the display text output, is the few kilobytes worth or so of the local-scale, on-the-fly target language model  206 . 
       FIG. 3  elaborates on the embodiment of  FIG. 2 .  FIG. 3  depicts language modeling system  300 , according to an illustrative embodiment which shares a number of common or analogous features with the illustrative embodiment of  FIG. 2 .  FIG. 3  includes grammar entry node  302 , grammar exit node  304 , target language model  306 , unigram back-off node  308  comprised in target language model  306 , and garbage language model  310 , implemented as an N-Gram Filler. Target language model  306  in this embodiment is built from a text output comprising a single sentence from a story. The sentence reads simply, “Giants are huge”. Target language model  306  includes a binary Context Free Grammar built from this single sentence. 
     In addition to the special nodes consisting of the grammar entry node  302 , grammar exit node  304 , and unigram back-off node  308 , target language model  306  includes three nodes corresponding to bigram states with one word each: bigram state node  312  for the word “Giants”, bigram state node  314  for the word “are”, and bigram state node  316  for the word “huge”. Running between the nodes are possible paths that may be taken, depending on the acoustic input. Each of grammar entry node  302  and bigram state nodes  312 ,  314 , and  316  have possible paths leading to unigram back-off node  308 , and thence to garbage language model  310  and back, if any one of the nodes is followed by a miscue. Target language model  306  may also include more complex N-gram nodes to provide stronger robustness, such as trigram nodes “&lt;s&gt; Giants”, “Giants Are”, “Are Huge”, “Huge &lt;/s&gt;” (not shown in  FIG. 3 ), in the example sentence “Giants are huge”. The domain-specific target language model may therefore be constructed with different complexity ranging from simple unigrams, to more complex bigrams and trigrams, to more complicated higher-order N-grams, in order to provide different levels of robustness in the system. Using a relatively simpler unigram or bigram garbage language model may provide significant advantages in efficiency. The complexity of the domain-specific target language model may be user-selectable, and may be selected according to the nature of the applications being implemented and the user&#39;s reading ability. 
     Grammar entry node  302 , bigram state nodes  312 ,  314 , and  316 , and grammar exit node  304  also have possible paths running in sequence between them, allowing for the potential match of the acoustic input with bigrams composed of the bigram state nodes  312 ,  314 , if the student correctly reads aloud (as embodied in the acoustic input) the two-word sequence “Giants are”, as well as the potential match of the acoustic input with bigrams composed of the bigram state nodes  314 ,  316 , if the student correctly reads aloud the two-word sequence “are huge”. 
     The garbage language model  310  is used to detect reading miscues, whether or not unforeseen, and without any need to predict in advance what the miscues will be like, to assemble and comb through a miscue database, or to try to decode miscues phonetically. This provides a particular advantage in a reading tutoring system for children, who are liable to say any variety of things or make any variety of sounds that have nothing to do with the displayed text output they are supposed to be reading. It is also advantageous for adult learners of a second language, as it detects the frequent miscues they may make such as by mispronouncing the words in the text output of the non-native language they are studying. 
     Garbage language model  310  may be obtained from a general-domain N-gram model, but restricted or trimmed down to a smaller selection from a dictation grammar, such as only the 1,600 most common words, for example. This is one example of a small selection that will reduce the processing burden on the system, that is nevertheless very effective. A small set of lexical features may be used by the garbage language model  310 , to save further on the processing burden. It has been found, for example, that basing the garbage language model  310  only on unigrams and bigrams provided very effective garbage language modeling, that was not substantially improved by trying to add additional, more burdensome lexical features to the language modeling. Garbage language model  310  with different complexity may be used, ranging from a simple unigram to more a more complex bigram or trigram, or higher order N-gram, although in some embodiments, higher orders of N-grams may provide diminishing returns in robustness while increasing the computational burden, such that building the garbage language model in a unigram or bigram form may provide the best efficiency for the goals for that embodiment. 
     Garbage language model  310  can thereby be built on-the-fly, during usage of the reading tutoring system; garbage language model  310  does not impose any burden to change decoding engines, and it can interface with any automatic speech recognition system; and it provides adjustable, tunable weighting parameters w 1 , w 2  that allow the sensitivity of the garbage language modeling, in terms of its Receiver Operating Characteristic (ROC) curve, to be freely adjusted, based on preference, the level of the student&#39;s knowledge, and so forth.  FIG. 5  depicts just such an ROC curve for garbage language model  310 , based on further discussion on the weighting parameters provided below in connection with  FIG. 4 . 
       FIG. 4  depicts a language modeling system  400  with analogous elements to those discussed above, although this one is directed to an equivalent two-path grammar for a single word. Language modeling system  400  includes grammar entry node  402 , grammar exit node  404 , target word node  406 , and garbage word node  410 . Language modeling system  400  also includes tunable weighting parameter w 0 , that applies to the path leading to garbage word node  410 . The weighting parameter w 0  can be calculated based on the weighting parameters w 1 , w 2  and the target language model  206  in  FIG. 2 . Garbage word node  410  is not limited to modeling words per se, but may also output garbage words that are acoustically similar to subword-level miscues that are detected, such as a partial word, a hesitation or elongation, or background noise, for example. 
     Given a spoken word acoustic input X, a target word T, and a garbage word G, a hypothesis testing scenario can be obtained as follows: 
     H 0 : Target word T exists; 
     H 1 : Target word T does not exist; 
     Then the decision rule is given by: 
     
       
         
           
             
               
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     where P(X|T) and P(X|G) are the acoustic score for the target and garbage words, respectively, and P(T) and P(G) are language model scores for the target and garbage words, respectively. The above decision rule is equivalent to the following decision rule: 
     
       
         
           
             
               
                 
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     where λ is a threshold as an explicit function of the weighting parameter w 0 . This detection scenario is equivalent to regular hypothesis testing in an utterance verification problem. 
     When the garbage model weight is increased, therefore, the miscue detection rate also increases, along with some increase in the rate of false alarms. The relationship between the prevalence of the two factors according to one illustrative implementation can be seen in  FIG. 5 , where the curve represents the rates of both detection and false alarm corresponding to a series of selected weighting parameters w 0  with a set of pre-trained acoustic model and language models. In general, it may often be desirable to train better acoustic and language models to obtain a curve towards the upper left corner of the graph, to work a good compromise between relatively high detection and relatively low false alarm rate. 
     For a fixed set of acoustic and language models, it may also be desired to adjust the weighting parameter to be more lenient, and occupy a spot on the curve more toward the lower left, such as for beginning students. This is to specifically avoid false alarms that might discourage these beginning students, at the expense of performance in the absolute detection rate. For more advanced students such as adult learners of a second language, and still assuming a fixed set of acoustic and language models, it may be preferable to adjust the weighting parameter w 0  to make the system more strict, i.e. to move the operating point toward the upper right portion of the curve in  FIG. 5 , when the students might be expected to understand the false alarms as such or to have more patience with them, but be more interested in addressing as many errors in reading as possible. 
     The miscues may be identified with one or more miscue categories, and the user-perceptible feedback may be based in part on one of the miscue categories with which a miscue in the acoustic input is identified, so that it will correct a mispronunciation, for example, but simply continue to prompt for an acoustic input if the miscue is an interjection or background noise. The miscue categories may include, for example, word repetition, breath, partial word, pause, hesitation or elongation, wrong word, mispronunciation, background noise, interjection or insertion, non-speech sound, and hyperarticulation. 
       FIG. 6  illustrates an example of a suitable computing system environment  600  on which various embodiments may be implemented. For example, various embodiments may be implemented as software applications, modules, or other forms of instructions that are executable by computing system environment  600  and that configure computing system environment  600  to perform various tasks or methods involved in different embodiments. A software application or module associated with an illustrative implementation of an automatic reading tutoring system with parallel polarized language modeling may be developed in any of a variety of programming or scripting languages or environments. For example, it may be written in C#, F#, C++, C, Pascal, Visual Basic, Java, JavaScript, Delphi, Eiffel, Nemerle, Perl, PHP, Python, Ruby, Visual FoxPro, Lua, or any other programming language. It is also envisioned that new programming languages and other forms of creating executable instructions will continue to be developed, in which further embodiments may readily be developed. 
     Computing system environment  600  as depicted in  FIG. 6  is only one example of a suitable computing environment for implementing various embodiments, and is not intended to suggest any limitation as to the scope of use or functionality of the claimed subject matter. Neither should the computing environment  600  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  600 . 
     Embodiments are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with various embodiments include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, telephony systems, distributed computing environments that include any of the above systems or devices, and the like. 
     Embodiments may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Some embodiments are designed to be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules are located in both local and remote computer storage media including memory storage devices. As described herein, such executable instructions may be stored on a medium such that they are capable of being read and executed by one or more components of a computing system, thereby configuring the computing system with new capabilities. 
     With reference to  FIG. 6 , an exemplary system for implementing some embodiments includes a general-purpose computing device in the form of a computer  610 . Components of computer  610  may include, but are not limited to, a processing unit  620 , a system memory  630 , and a system bus  621  that couples various system components including the system memory to the processing unit  620 . The system bus  621  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
     Computer  610  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  610  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media include both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  610 . 
     Communication media typically embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
     The system memory  630  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  631  and random access memory (RAM)  632 . A basic input/output system  633  (BIOS), containing the basic routines that help to transfer information between elements within computer  610 , such as during start-up, is typically stored in ROM  631 . RAM  632  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  620 . By way of example and not limitation,  FIG. 6  illustrates operating system  634 , application programs  635 , other program modules  636 , and program data  637 . 
     The computer  610  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example and not of limitation,  FIG. 6  illustrates a hard disk drive  641  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  651  that reads from or writes to a removable, nonvolatile magnetic disk  652 , and an optical disk drive  655  that reads from or writes to a removable, nonvolatile optical disk  656  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  641  is typically connected to the system bus  621  through a non-removable memory interface such as interface  640 , and magnetic disk drive  651  and optical disk drive  655  are typically connected to the system bus  621  by a removable memory interface, such as interface  650 . 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 6 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  610 . In  FIG. 6 , for example, hard disk drive  641  is illustrated as storing operating system  644 , application programs  645 , other program modules  646 , and program data  647 . Note that these components can either be the same as or different from operating system  634 , application programs  635 , other program modules  636 , and program data  637 . Operating system  644 , application programs  645 , other program modules  646 , and program data  647  are given different numbers here to illustrate that, at a minimum, they may be different copies. 
     A user may enter commands and information into the computer  610  through input devices such as a keyboard  662 , a microphone  663 , and a pointing device  661 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  620  through a user input interface  660  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  691  or other type of display device is also connected to the system bus  621  via an interface, such as a video interface  690 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  697  and printer  696 , which may be connected through an output peripheral interface  695 . 
     The computer  610  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  680 . The remote computer  680  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  610 . The logical connections depicted in  FIG. 6  include a local area network (LAN)  671  and a wide area network (WAN)  673 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  610  is connected to the LAN  671  through a network interface or adapter  670 . When used in a WAN networking environment, the computer  610  typically includes a modem  672  or other means for establishing communications over the WAN  673 , such as the Internet. The modem  672 , which may be internal or external, may be connected to the system bus  621  via the user input interface  660 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  610 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 6  illustrates remote application programs  685  as residing on remote computer  680 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
       FIG. 7  depicts a block diagram of a general mobile computing environment  700 , comprising a mobile computing device  701  and a medium, readable by the mobile computing device and comprising executable instructions that are executable by the mobile computing device, according to another illustrative embodiment.  FIG. 7  depicts a block diagram of a mobile computing system  700  including mobile device  701 , according to an illustrative embodiment. Mobile device  701  includes a microprocessor  702 , memory  704 , input/output (I/O) components  706 , and a communication interface  708  for communicating with remote computers or other mobile devices. In one embodiment, the afore-mentioned components are coupled for communication with one another over a suitable bus  710 . 
     Memory  704  is implemented as non-volatile electronic memory such as random access memory (RAM) with a battery back-up module (not shown) such that information stored in memory  704  is not lost when the general power to mobile computing device  701  is shut down. A portion of memory  704  is illustratively allocated as addressable memory for program execution, while another portion of memory  704  is illustratively used for storage, such as to simulate storage on a disk drive. 
     Memory  704  includes an operating system  712 , application programs  714  as well as an object store  716 . During operation, operating system  712  is illustratively executed by processor  702  from memory  704 . Operating system  712 , in one illustrative embodiment, is a WINDOWS® CE brand operating system commercially available from Microsoft Corporation. Operating system  712  is illustratively designed for mobile devices, and implements database features that can be utilized by applications  714  through a set of exposed application programming interfaces and methods. The objects in object store  716  are maintained by applications  714  and operating system  712 , at least partially in response to calls to the exposed application programming interfaces and methods. 
     Communication interface  708  represents numerous devices and technologies that allow mobile computing device  701  to send and receive information. The devices include wired and wireless modems, satellite receivers and broadcast tuners to name a few. Mobile computing device  701  can also be directly connected to a computer to exchange data therewith. In such cases, communication interface  708  can be an infrared transceiver or a serial or parallel communication connection, all of which are capable of transmitting streaming information. 
     Input/output components  706  include a variety of input devices such as a touch-sensitive screen, buttons, rollers, and a microphone as well as a variety of output devices including an audio generator, a vibrating device, and a display. The devices listed above are by way of example and need not all be present on mobile computing device  701 . In addition, other input/output devices may be attached to or found with mobile computing device  701 . 
     Mobile computing environment  700  also includes network  720 . Mobile computing device  701  is illustratively in wireless communication with network  720 —which may be the Internet, a wide area network, or a local area network, for example—by sending and receiving electromagnetic signals  799  of a suitable protocol between communication interface  708  and wireless interface  722 . Wireless interface  722  may be a wireless hub or cellular antenna, for example, or any other signal interface. Wireless interface  722  in turn provides access via network  720  to a wide array of additional computing resources, illustratively represented by computing resources  724  and  726 . Naturally, any number of computing devices in any locations may be in communicative connection with network  720 . Mobile computing device  701  is enabled to make use of executable instructions stored on the media of memory component  704 , such as executable instructions that enable mobile computing device  701  to implement various functions of automatic reading tutoring with parallel polarized language modeling, in an illustrative embodiment. 
     Although the subject matter has been described in language specific to certain illustrative structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as illustrative examples of ways in which the claims may be implemented. As a particular example, while the terms “computer”, “computing device”, or “computing system” may herein sometimes be used alone for convenience, it is well understood that each of these could refer to any computing device, computing system, computing environment, mobile device, or other information processing component or context, and is not limited to any individual interpretation. As another particular example, while many embodiments are presented with illustrative elements that are widely familiar at the time of filing the patent application, it is envisioned that many new innovations in computing technology will affect elements of different embodiments, in such aspects as user interfaces, user input methods, computing environments, and computing methods, and that the elements defined by the claims may be embodied according to these and other innovative advances in accordance with the developing understanding of those skilled in the art, while still remaining consistent with and encompassed by the subject matter defined by the claims herein.