Patent Publication Number: US-2023162731-A1

Title: Voice-to-text data processing

Description:
RELATED APPLICATIONS 
     This application is a continuation of PCT application serial no. PCT/CN2021/132004 filed Nov. 22, 2021, which is hereby incorporated herein in its entirety by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to computing systems, and more particularly, to voice-to-text data processing. 
     BACKGROUND 
     In a typical work day, users may call or email an ever-increasing number of people using their computing devices. Speech recognition technology is leveraged today to improve operational performance of computing devices and improve the user experience with those devices. 
     Such devices implement speech recognition technologies to recognize and translate words spoken by users into text. In short, speech recognition enables a program to process human speech into a written format. Speech recognition enhances digital communications by reducing complexity and effort to perform such communications through the use of words spoken by users as a means of input to communicate. 
     SUMMARY 
     A method includes converting, based on rules, a word spoken by a user into a pattern of pronunciation symbols in response to an unsuccessful attempt to retrieve the word in a list. The pattern of pronunciation symbols provide a visual representation of speech sounds identifying the word in the list. The pattern of pronunciation symbols of the converted word are compared to a database of patterns, with the patterns in the database being in a format of pronunciation symbols corresponding to the words in the list. Each pattern used in the compare has a match value assigned thereto based on being compared to the pattern of pronunciation symbols of the converted word. The word in the list corresponding to the pattern having the match value that is indicative of 
     The word spoken by the user may be converted into alphabet characters before converting into the pattern of pronunciation symbols. The unsuccessful attempt to retrieve the word in the list may be based on there not being a match between the alphabet characters representing the spoken word to alphabet characters representing the words in the list. 
     The method may further include ranking the match values assigned to the patterns used in the compare, and selecting the pattern having a highest ranked match value that exceeds a threshold. 
     The patterns in the database may include a plurality of multi-lingual patterns, with each language pattern in the multi-lingual patterns being based on a particular language pronunciation of the word in the list. Each word in the list may be represented by more than one language pattern. 
     The compare may start with a first one of language patterns having a particular language pronunciation, and in response to there not being a match, repeat the compare with a second one of the language patterns having a different particular language pronunciation. 
     In response to there being a match with one of the language patterns having a particular language pronunciation, the method may further include adding the language pattern providing the match to a custom pattern section in the database, and for a next time a word is spoken by the user for retrieval, starting the compare using the language pattern in the custom pattern section. In response to there not being a match with the language pattern in the custom pattern section, the compare is continued with the other language patterns in the database. 
     Performing the compare may include dividing the pattern of pronunciation symbols into pronunciation sections for the converted word, and dividing the pattern of pronunciation symbols into pronunciation sections for each pattern in the database used in the compare. The pronunciation sections for the converted word may then be compared to the corresponding pronunciation sections for each pattern used in the compare. 
     The matching value assigned to each pattern used in the compare may be based on a respective similarity value assigned to each pronunciation section. The matching value assigned to each pattern may be determined by adding the respective similarity values assigned to the pronunciation sections for the pattern, and dividing the added respective similarity values by a number of the pronunciation sections in the converted word. 
     Each pronunciation section may include a plurality of letters, and wherein the respective similarity value assigned to each pronunciation section for the pattern used in the compare is based determining a similarity value for each letter in the pronunciation section for the pattern used in the compare, and multiplying the similarity value for each letter in the pronunciation section by a respective weighting factor. The determined similarity value for each letter multiplied by the respective weighting factor are added together to determine the similarity value assigned to each pronunciation section. 
     The rules for converting the word into a pattern of pronunciation symbols may be based on an international phonetic alphabet (IPA). Retrieval of the word spoken may be initiated by the user in response to the user speaking a predetermined word. 
     Another aspect is directed to a computing device implementing the method as described above. The computing device includes a memory and a processor configured to cooperate with the memory. The processor is configured to convert, based on rules, a word spoken by a user into a pattern of pronunciation symbols in response to an unsuccessful attempt to retrieve the word in a list. The pattern of pronunciation symbols provides a visual representation of speech sounds identifying the word in the list. The pattern of pronunciation symbols of the converted word are compared to a database of patterns, with the patterns in the database being in a format of pronunciation symbols corresponding to the words in the list. Each pattern used in the compare has a match value assigned thereto based on being compared to the pattern of pronunciation symbols of the converted word. The word in the list corresponding to the pattern having the match value that is indicative of a match to the converted word is provided to the user. 
     Yet another aspect is directed to a non-transitory computer readable medium for a computing device, and with the non-transitory computer readable medium having a plurality of computer executable instructions for causing the computing device to perform steps as described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic block diagram of a network environment of computing devices in which various aspects of the disclosure may be implemented. 
         FIG.  2    is a schematic block diagram of a computing device useful for practicing an embodiment of the client machines or the remote machines illustrated in  FIG.  1   . 
         FIG.  3    is a schematic block diagram of a cloud computing environment in which various aspects of the disclosure may be implemented. 
         FIG.  4    is a schematic block diagram of desktop, mobile and web based devices operating a workspace app in which various aspects of the disclosure may be implemented. 
         FIG.  5    is a schematic block diagram of a workspace network environment of computing devices in which various aspects of the disclosure may be implemented. 
         FIG.  6    is a schematic block diagram of a computing device using multi-lingual voice patterns according to aspects of the disclosure. 
         FIG.  7    are results based on determining match values using the computing device illustrated in  FIG.  6   . 
         FIG.  8    is an equation for determining a similarity value per pronunciation section for the example in  FIG.  7   . 
         FIG.  9    is an equation for determining the match value by adding together the similarity values per pronunciation section as determined with the equation in  FIG.  8   . 
         FIG.  10    is a detailed flow diagram for operating the computing device illustrated in  FIG.  6   . 
         FIG.  11    is a high-level flow diagram for operating the computing device illustrated in  FIG.  6   . 
     
    
    
     DETAILED DESCRIPTION 
     The present description is made with reference to the accompanying drawings, in which exemplary embodiments are shown. However, many different embodiments may be used, and thus the description should not be construed as limited to the particular embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. Like numbers refer to like elements throughout, and prime notation is used to indicate similar elements in different embodiments. 
     Speech recognition technology that converts spoken words to text typically uses a voice-to-text conversion. The effectiveness of speech recognition technology is often determined based on the accuracy of the translation of the spoken word (e.g., a word error rate). Several factors can adversely impact accuracy, such as pronunciation, accent, pitch, volume, etc. To improve the accuracy of speech recognition technology one or more of these factors need to be addressed. Otherwise, the technology becomes less useful in general, and in particular, the application of generated text by other technologies becomes very limited or non-existent. 
     Multi-lingual voice patterns may be used to improve voice-to-text data processing. The voice patterns for selected words in a list are pronounced in different languages. The voice patterns are based on patterns of symbols (e.g., pronunciation symbols) providing a visual representation of speech sounds identifying the words in the list. The use of different language patterns representing the words in the list improves the robustness of performing an action (e.g., retrieving a contact) regardless of the user&#39;s native language and any variations of the user pronouncing the word to be retrieved. 
     Referring initially to  FIG.  1   , a non-limiting network environment  10  in which various aspects of the disclosure may be implemented includes one or more client machines  12 A- 12 N, one or more remote machines  16 A- 16 N, one or more networks  14 ,  14 ′, and one or more appliances  18  installed within the computing environment  10 . The client machines  12 A- 12 N communicate with the remote machines  16 A- 16 N via the networks  14 ,  14 ′. In some embodiments, the client machines  12 A- 12 N communicate with the remote machines  16 A- 16 N via an intermediary appliance  18 . The illustrated appliance  18  is positioned between the networks  14 ,  14 ′ and may also be referred to as a network interface or gateway. In some embodiments, the appliance  18  may operate as an application delivery controller (ADC) to provide clients with access to business applications and other data deployed in a data center, the cloud, or delivered as Software as a Service (SaaS) across a range of client devices, and/or provide other functionality such as load balancing, etc. In some embodiments, multiple appliances  18  may be used, and the appliance(s)  18  may be deployed as part of the network  14  and/or  14 ′. 
     The client machines  12 A- 12 N may be generally referred to as client machines  12 , local machines  12 , clients  12 , client nodes  12 , client computers  12 , client devices  12 , computing devices  12 , endpoints  12 , or endpoint nodes  12 . The remote machines  16 A- 16 N may be generally referred to as servers  16  or a server farm  16 . In some embodiments, a client device  12  may have the capacity to function as both a client node seeking access to resources provided by a server  16  and as a server  16  providing access to hosted resources for other client devices  12 A- 12 N. The networks  14 ,  14 ′ may be generally referred to as a network  14 . The networks  14  may be configured in any combination of wired and wireless networks. 
     A server  16  may be any server type such as, for example: a file server; an application server; a web server; a proxy server; an appliance; a network appliance; a gateway; an application gateway; a gateway server; a virtualization server; a deployment server; a Secure Sockets Layer Virtual Private Network (SSL VPN) server; a firewall; a web server; a server executing an active directory; a cloud server; or a server executing an application acceleration program that provides firewall functionality, application functionality, or load balancing functionality. 
     A server  16  may execute, operate or otherwise provide an application that may be any one of the following: software; a program; executable instructions; a virtual machine; a hypervisor; a web browser; a web-based client; a client-server application; a thin-client computing client; an ActiveX control; a Java applet; software related to voice over internet protocol (VoIP) communications like a soft IP telephone; an application for streaming video and/or audio; an application for facilitating real-time-data communications; a HTTP client; a FTP client; an Oscar client; a Telnet client; or any other set of executable instructions. 
     In some embodiments, a server  16  may execute a remote presentation services program or other program that uses a thin-client or a remote-display protocol to capture display output generated by an application executing on a server  16  and transmit the application display output to a client device  12 . 
     In yet other embodiments, a server  16  may execute a virtual machine providing, to a user of a client device  12 , access to a computing environment. The client device  12  may be a virtual machine. The virtual machine may be managed by, for example, a hypervisor, a virtual machine manager (VMM), or any other hardware virtualization technique within the server  16 . 
     In some embodiments, the network  14  may be: a local-area network (LAN); a metropolitan area network (MAN); a wide area network (WAN); a primary public network  14 ; and a primary private network  14 . Additional embodiments may include a network  14  of mobile telephone networks that use various protocols to communicate among mobile devices. For short range communications within a wireless local-area network (WLAN), the protocols may include 802.11, Bluetooth, and Near Field Communication (NFC). 
       FIG.  2    depicts a block diagram of a computing device  20  useful for practicing an embodiment of client devices  12 , appliances  18  and/or servers  16 . The computing device  20  includes one or more processors  22 , volatile memory  24  (e.g., random access memory (RAM)), non-volatile memory  30 , user interface (UI)  38 , one or more communications interfaces  26 , and a communications bus  48 . 
     The non-volatile memory  30  may include: one or more hard disk drives (HDDs) or other magnetic or optical storage media; one or more solid state drives (SSDs), such as a flash drive or other solid-state storage media; one or more hybrid magnetic and solid-state drives; and/or one or more virtual storage volumes, such as a cloud storage, or a combination of such physical storage volumes and virtual storage volumes or arrays thereof. 
     The user interface  38  may include a graphical user interface (GUI)  40  (e.g., a touchscreen, a display, etc.) and one or more input/output (I/O) devices  42  (e.g., a mouse, a keyboard, a microphone, one or more speakers, one or more cameras, one or more biometric scanners, one or more environmental sensors, and one or more accelerometers, etc.). 
     The non-volatile memory  30  stores an operating system  32 , one or more applications  34 , and data  36  such that, for example, computer instructions of the operating system  32  and/or the applications  34  are executed by processor(s)  22  out of the volatile memory  24 . In some embodiments, the volatile memory  24  may include one or more types of RAM and/or a cache memory that may offer a faster response time than a main memory. Data may be entered using an input device of the GUI  40  or received from the I/O device(s)  42 . Various elements of the computer  20  may communicate via the communications bus  48 . 
     The illustrated computing device  20  is shown merely as an example client device or server, and may be implemented by any computing or processing environment with any type of machine or set of machines that may have suitable hardware and/or software capable of operating as described herein. 
     The processor(s)  22  may be implemented by one or more programmable processors to execute one or more executable instructions, such as a computer program, to perform the functions of the system. As used herein, the term “processor” describes circuitry that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations may be hard coded into the circuitry or soft coded by way of instructions held in a memory device and executed by the circuitry. A processor may perform the function, operation, or sequence of operations using digital values and/or using analog signals. 
     In some embodiments, the processor can be embodied in one or more application specific integrated circuits (ASICs), microprocessors, digital signal processors (DSPs), graphics processing units (GPUs), microcontrollers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), multi-core processors, or general-purpose computers with associated memory. 
     The processor  22  may be analog, digital or mixed-signal. In some embodiments, the processor  22  may be one or more physical processors, or one or more virtual (e.g., remotely located or cloud) processors. A processor including multiple processor cores and/or multiple processors may provide functionality for parallel, simultaneous execution of instructions or for parallel, simultaneous execution of one instruction on more than one piece of data. 
     The communications interfaces  26  may include one or more interfaces to enable the computing device  20  to access a computer network such as a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or the Internet through a variety of wired and/or wireless connections, including cellular connections. 
     In described embodiments, the computing device  20  may execute an application on behalf of a user of a client device. For example, the computing device  20  may execute one or more virtual machines managed by a hypervisor. Each virtual machine may provide an execution session within which applications execute on behalf of a user or a client device, such as a hosted desktop session. The computing device  20  may also execute a terminal services session to provide a hosted desktop environment. The computing device  20  may provide access to a remote computing environment including one or more applications, one or more desktop applications, and one or more desktop sessions in which one or more applications may execute. 
     An example virtualization server  16  may be implemented using Citrix Hypervisor provided by Citrix Systems, Inc., of Fort Lauderdale, Fla. (“Citrix Systems”). Virtual app and desktop sessions may further be provided by Citrix Virtual Apps and Desktops (CVAD), also from Citrix Systems. Citrix Virtual Apps and Desktops is an application virtualization solution that enhances productivity with universal access to virtual sessions including virtual app, desktop, and data sessions from any device, plus the option to implement a scalable VDI solution. Virtual sessions may further include Software as a Service (SaaS) and Desktop as a Service (DaaS) sessions, for example. 
     Referring to  FIG.  3   , a cloud computing environment  50  is depicted, which may also be referred to as a cloud environment, cloud computing or cloud network. The cloud computing environment  50  can provide the delivery of shared computing services and/or resources to multiple users or tenants. For example, the shared resources and services can include, but are not limited to, networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, databases, software, hardware, analytics, and intelligence. 
     In the cloud computing environment  50 , one or more clients  52 A- 52 C (such as those described above) are in communication with a cloud network  54 . The cloud network  54  may include backend platforms, e.g., servers, storage, server farms or data centers. The users or clients  52 A- 52 C can correspond to a single organization/tenant or multiple organizations/tenants. More particularly, in one example implementation the cloud computing environment  50  may provide a private cloud serving a single organization (e.g., enterprise cloud). In another example, the cloud computing environment  50  may provide a community or public cloud serving multiple organizations/tenants. In still further embodiments, the cloud computing environment  50  may provide a hybrid cloud that is a combination of a public cloud and a private cloud. Public clouds may include public servers that are maintained by third parties to the clients  52 A- 52 C or the enterprise/tenant. The servers may be located off-site in remote geographical locations or otherwise. 
     The cloud computing environment  50  can provide resource pooling to serve multiple users via clients  52 A- 52 C through a multi-tenant environment or multi-tenant model with different physical and virtual resources dynamically assigned and reassigned responsive to different demands within the respective environment. The multi-tenant environment can include a system or architecture that can provide a single instance of software, an application or a software application to serve multiple users. In some embodiments, the cloud computing environment  50  can provide on-demand self-service to unilaterally provision computing capabilities (e.g., server time, network storage) across a network for multiple clients  52 A- 52 C. The cloud computing environment  50  can provide an elasticity to dynamically scale out or scale in responsive to different demands from one or more clients  52 . In some embodiments, the computing environment  50  can include or provide monitoring services to monitor, control and/or generate reports corresponding to the provided shared services and resources. 
     In some embodiments, the cloud computing environment  50  may provide cloud-based delivery of different types of cloud computing services, such as Software as a service (SaaS)  56 , Platform as a Service (PaaS)  58 , Infrastructure as a Service (IaaS)  60 , and Desktop as a Service (DaaS)  62 , for example. IaaS may refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS include AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif. 
     PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Wash., Google App Engine provided by Google Inc., and HEROKU provided by Heroku, Inc. of San Francisco, Calif. 
     SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc., SALESFORCE provided by Salesforce.com Inc. of San Francisco, Calif., or OFFICE 365 provided by Microsoft Corporation. Examples of SaaS may also include data storage providers, e.g. DROPBOX provided by Dropbox, Inc. of San Francisco, Calif., Microsoft ONEDRIVE provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif. 
     Similar to SaaS, DaaS (which is also known as hosted desktop services) is a form of virtual desktop infrastructure (VDI) in which virtual desktop sessions are typically delivered as a cloud service along with the apps used on the virtual desktop. Citrix Cloud is one example of a DaaS delivery platform. DaaS delivery platforms may be hosted on a public cloud computing infrastructure such as AZURE CLOUD from Microsoft Corporation of Redmond, Wash. (herein “Azure”), or AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash. (herein “AWS”), for example. In the case of Citrix Cloud, Citrix Workspace app may be used as a single-entry point for bringing apps, files and desktops together (whether on-premises or in the cloud) to deliver a unified experience. 
     The unified experience provided by the Citrix Workspace app will now be discussed in greater detail with reference to  FIG.  4   . The Citrix Workspace app will be generally referred to herein as the workspace app  70 . The workspace app  70  is how a user gets access to their workspace resources, one category of which is applications. These applications can be SaaS apps, web apps or virtual apps. The workspace app  70  also gives users access to their desktops, which may be a local desktop or a virtual desktop. Further, the workspace app  70  gives users access to their files and data, which may be stored in numerous repositories. The files and data may be hosted on Citrix ShareFile, hosted on an on-premises network file server, or hosted in some other cloud storage provider, such as Microsoft OneDrive or Google Drive Box, for example. 
     To provide a unified experience, all of the resources a user requires may be located and accessible from the workspace app  70 . The workspace app  70  is provided in different versions. One version of the workspace app  70  is an installed application for desktops  72 , which may be based on Windows, Mac or Linux platforms. A second version of the workspace app  70  is an installed application for mobile devices  74 , which may be based on iOS or Android platforms. A third version of the workspace app  70  uses a hypertext markup language (HTML) browser to provide a user access to their workspace environment. The web version of the workspace app  70  is used when a user does not want to install the workspace app or does not have the rights to install the workspace app, such as when operating a public kiosk  76 . 
     Each of these different versions of the workspace app  70  may advantageously provide the same user experience. This advantageously allows a user to move from client device  72  to client device  74  to client device  76  in different platforms and still receive the same user experience for their workspace. The client devices  72 ,  74  and  76  are referred to as endpoints. 
     As noted above, the workspace app  70  supports Windows, Mac, Linux, iOS, and Android platforms as well as platforms with an HTML browser (HTML5). The workspace app  70  incorporates multiple engines  80 - 90  allowing users access to numerous types of app and data resources. Each engine  80 - 90  optimizes the user experience for a particular resource. Each engine  80 - 90  also provides an organization or enterprise with insights into user activities and potential security threats. 
     An embedded browser engine  80  keeps SaaS and web apps contained within the workspace app  70  instead of launching them on a locally installed and unmanaged browser. With the embedded browser, the workspace app  70  is able to intercept user-selected hyperlinks in SaaS and web apps and request a risk analysis before approving, denying, or isolating access. 
     A high definition experience (HDX) engine  82  establishes connections to virtual browsers, virtual apps and desktop sessions running on either Windows or Linux operating systems. With the HDX engine  82 , Windows and Linux resources run remotely, while the display remains local, on the endpoint. To provide the best possible user experience, the HDX engine  82  utilizes different virtual channels to adapt to changing network conditions and application requirements. To overcome high-latency or high-packet loss networks, the HDX engine  82  automatically implements optimized transport protocols and greater compression algorithms. Each algorithm is optimized for a certain type of display, such as video, images, or text. The HDX engine  82  identifies these types of resources in an application and applies the most appropriate algorithm to that section of the screen. 
     For many users, a workspace centers on data. A content collaboration engine  84  allows users to integrate all data into the workspace, whether that data lives on-premises or in the cloud. The content collaboration engine  84  allows administrators and users to create a set of connectors to corporate and user-specific data storage locations. This can include OneDrive, Dropbox, and on-premises network file shares, for example. Users can maintain files in multiple repositories and allow the workspace app  70  to consolidate them into a single, personalized library. 
     A networking engine  86  identifies whether or not an endpoint or an app on the endpoint requires network connectivity to a secured backend resource. The networking engine  86  can automatically establish a full VPN tunnel for the entire endpoint device, or it can create an app-specific p-VPN connection. A p-VPN defines what backend resources an application and an endpoint device can access, thus protecting the backend infrastructure. In many instances, certain user activities benefit from unique network-based optimizations. If the user requests a file copy, the workspace app  70  can automatically utilize multiple network connections simultaneously to complete the activity faster. If the user initiates a VoIP call, the workspace app  70  improves its quality by duplicating the call across multiple network connections. The networking engine  86  uses only the packets that arrive first. 
     An analytics engine  88  reports on the user&#39;s device, location and behavior, where cloud-based services identify any potential anomalies that might be the result of a stolen device, a hacked identity or a user who is preparing to leave the company. The information gathered by the analytics engine  88  protects company assets by automatically implementing counter-measures. 
     A management engine  90  keeps the workspace app  70  current. This not only provides users with the latest capabilities, but also includes extra security enhancements. The workspace app  70  includes an auto-update service that routinely checks and automatically deploys updates based on customizable policies. 
     Referring now to  FIG.  5   , a workspace network environment  100  providing a unified experience to a user based on the workspace app  70  will be discussed. The desktop, mobile and web versions of the workspace app  70  all communicate with the workspace experience service  102  running within the Citrix Cloud  104 . The workspace experience service  102  then pulls in all the different resource feeds via a resource feed micro-service  108 . That is, all the different resources from other services running in the Citrix Cloud  104  are pulled in by the resource feed micro-service  108 . The different services may include a virtual apps and desktop service  110 , a secure browser service  112 , an endpoint management service  114 , a content collaboration service  116 , and an access control service  118 . Any service that an organization or enterprise subscribes to are automatically pulled into the workspace experience service  102  and delivered to the user&#39;s workspace app  70 . 
     In addition to cloud feeds  120 , the resource feed micro-service  108  can pull in on-premises feeds  122 . A cloud connector  124  is used to provide virtual apps and desktop deployments that are running in an on-premises data center. Desktop virtualization may be provided by Citrix virtual apps and desktops  126 , Microsoft RDS  128  or VMware Horizon  130 , for example. In addition to cloud feeds  120  and on-premises feeds  122 , device feeds  132  from Internet of Thing (IoT) devices  134 , for example, may be pulled in by the resource feed micro-service  108 . Site aggregation is used to tie the different resources into the user&#39;s overall workspace experience. 
     The cloud feeds  120 , on-premises feeds  122  and device feeds  132  each provides the user&#39;s workspace experience with a different and unique type of application. The workspace experience can support local apps, SaaS apps, virtual apps, and desktops browser apps, as well as storage apps. As the feeds continue to increase and expand, the workspace experience is able to include additional resources in the user&#39;s overall workspace. This means a user will be able to get to every single application that they need access to. 
     Still referring to the workspace network environment  20 , a series of events will be described on how a unified experience is provided to a user. The unified experience starts with the user using the workspace app  70  to connect to the workspace experience service  102  running within the Citrix Cloud  104 , and presenting their identity (event 1). The identity includes a user name and password, for example. 
     The workspace experience service  102  forwards the user&#39;s identity to an identity micro-service  140  within the Citrix Cloud  104  (event 2). The identity micro-service  140  authenticates the user to the correct identity provider  142  (event 3) based on the organization&#39;s workspace configuration. Authentication may be based on an on-premises active directory  144  that requires the deployment of a cloud connector  146 . Authentication may also be based on Azure Active Directory  148  or even a third party identity provider  150 , such as Citrix ADC or Okta, for example. 
     Once authorized, the workspace experience service  102  requests a list of authorized resources (event 4) from the resource feed micro-service  108 . For each configured resource feed  106 , the resource feed micro-service  108  requests an identity token (event 5) from the single-sign micro-service  152 . 
     The resource feed specific identity token is passed to each resource&#39;s point of authentication (event 6). On-premises resources  122  are contacted through the Citrix Cloud Connector  124 . Each resource feed  106  replies with a list of resources authorized for the respective identity (event 7). 
     The resource feed micro-service  108  aggregates all items from the different resource feeds  106  and forwards (event 8) to the workspace experience service  102 . The user selects a resource from the workspace experience service  102  (event 9). 
     The workspace experience service  102  forwards the request to the resource feed micro-service  108  (event 10). The resource feed micro-service  108  requests an identity token from the single sign-on micro-service  152  (event 11). The user&#39;s identity token is sent to the workspace experience service  102  (event 12) where a launch ticket is generated and sent to the user. 
     The user initiates a secure session to a gateway service  160  and presents the launch ticket (event 13). The gateway service  160  initiates a secure session to the appropriate resource feed  106  and presents the identity token to seamlessly authenticate the user (event 14). Once the session initializes, the user is able to utilize the resource (event 15). Having an entire workspace delivered through a single access point or application advantageously improves productivity and streamlines common workflows for the user. 
     Referring now to  FIG.  6   , a computing device  300  that uses multi-lingual voice patterns to search for words  344  in a list  342  will be discussed, wherein the voice patterns are generated from the list  342  and pronounced in different languages. The voice patterns are based on patterns of pronunciation symbols providing a visual representation or cue of speech sounds identifying the words (e.g., contacts)  344  in the list  342 . Individual words  344  in the list  342  may be represented by more than one language pattern. The use of different language patterns representing the words  344  in the list  342  improves the robustness of performing an action (e.g., retrieving a contact) regardless of the user&#39;s native language and any variations of the user pronouncing the word to be retrieved. 
     The patterns of pronunciations symbols may be based on the International Phonetic Alphabet (IPA). IPA is a phonetic notation system. that uses a set of pronunciation symbols to represent distinct sounds that exists in. human spoken languages. 
     IPA symbols are composed of one or more elements of two basic types, letters and diacritics. A diacritic is a sign, such as an accent or cedilla, which when written above or below a letter indicates a difference in pronunciation from the same letter when unmarked or differently marked. For example, the sound of the English letter  t  may be transcribed in IPA with a single letter, [t], or with a letter plus diacritics, [   h ], depending on how precise one wishes to be. As another example, pronunciation of the name Baron in English is ‘bær. n while the Japanese pronunciation is bΛr n. 
     The patterns of pronunciation symbols advantageously allow a word (e.g., a contact)  344  to be retrieved when the word cannot be pronounced correctly, as is typically the case with a non-English speaking user. For example, a Chinese employee within an organization may have difficulty pronouncing a word  344  written in alphabet characters, such as when the word is a Spanish or Indian name, for example. If the word  344  is to be searched using alphabet characters, then the search will not be successful since an exact match is required. As an example, if the Indian name Dipankar is to be retrieved but the Chinese employee does not pronounce Dipankar correctly, then the voice-to-text conversion of what the Chinese employee says will not be a match with the name Dipankar in the list  342 . 
     As will be discussed in greater detail below, in response to an unsuccessful attempt to retrieve the word  344  using alphabet characters, the word is converted to a pattern of symbols (e.g., pronunciation symbols). Alternatively, converting the word to a pattern of symbols may be done parallel with the voice-to-text conversion. The pattern of symbols for the converted word is then compared to the pattern of symbols for the different languages representing the words  344  in the list  342  for a match. 
     Still referring to  FIG.  6   , the computing device  300  includes a microphone  310  that receives a word (e.g., the name of the contact)  344  to be retrieved from the list  344  as spoken by the user. The list  342  is stored in memory  340 , with the words being represented using alphabet characters. 
     For example, in response to the user speaking a word (e.g., a predetermined word), such as “call” or “email,” for example, followed by the name of the contact to be retrieved, a processor  322  coupled to the microphone  310  performs voice-to-text conversion  324  on the spoken name. The voice-to-text conversion  324  converts the spoken name to alphabet characters. 
     The processor  322  compares the converted word in alphabet characters to the words  344  in the list  342  that are also in alphabet characters. If a match is found, the retrieved word is displayed for user verification. For example, the retrieved word can be a name of a contact and that name is displayed on a display  330  coupled to the processor  322 . 
     To successfully retrieve the word  344  using voice-to-text conversion  324 , the word needs to be pronounced correctly by the user. If the word  344  is not pronounced correctly, the word will not be retrieved. An incorrect pronunciation may occur when a non-English speaking user has difficulty pronouncing the word  344  written in alphabet characters because of the speaker&#39;s unfamiliarity with the language. 
     In response to an unsuccessful attempt to retrieve the word  344  in the list  342  based on there not being an exact match between the alphabet characters representing the spoken word to alphabet characters representing the words  344  in the list  342 , the processor  322  uses voice-to-pronunciation symbols conversion  326  to convert the spoken word into a pattern of symbols (e.g., pronunciation symbols). The pattern of symbols provide a visual representation of speech sounds identifying the word  344  in the list  342 . 
     The processor  322  compares the pattern of symbols of the converted word to a database  350 , which may also be stored in the memory  340 . The database  350  may include multi-lingual voice patterns  352 - 356  representing the words  344  in the list  342  in different languages. The patterns in the database  352 - 356  are in a format of pronunciation symbols corresponding to the words  344  in the list  342 . Individual words  344  in the list  342  may be represented by more than one language pattern. 
     Individual patterns in the database  350  used in the compare have match values assigned thereto based on being compared to the pattern of symbols of the converted word. The word  344  in the list  342  corresponding to the pattern having the match value that is indicative of a match to the converted word is retrieved for display to the user. This is based on the processor  322  ranking the match values assigned to the patterns used in the compare, and selecting the pattern having a highest ranked match value that exceeds a threshold. 
     Referring now to  FIG.  7   , an example retrieval of a word (e.g., a contact)  344  from the list  342  based on determining match values with the patterns of symbols representing the words  344  in the user&#39;s list  342  will be discussed. In this example, the patterns of symbols have an English pronunciation. 
     The word to be retrieved as spoken by the user is Kelly  360 . The pattern of pronunciation symbols representing Kelly  360  is Kε li. The pattern of symbols for any word may be divided into pronunciation sections. For Kelly  360 , Kε  362  is the 1 st  pronunciation section and li  364  is the 2 nd  pronunciation section li  364 . 
     The pattern of symbols for Kelly  360  will be compared to the pattern of pronunciation symbols for the following words  344  in the list  342 : Johnson  366 , Michael  368 , Kelly  370 , Karen  372  and Catherine  374 . Words  366 - 374  are likewise divided into pronunciation sections. Catherine  374  has three pronunciation sections whereas the other contacts have two pronunciation sections. 
     Two equations are used to determine the match values. Equation (1)  380  as provided in  FIG.  8    is used to determine a value (e.g., a similarity value) for individual pronunciation sections. Equation (2)  390  as provided in  FIG.  9    is used to determine the match value (e.g., a combined value) by adding together the similarity values per pronunciation section divided by the number of pronunciation sections in the converted contact to be match, which for Kelly  360  is two pronunciation sections. 
     The letters in individual pronunciation sections includes a constant, a vowel, and possibly additional constants or vowels. In the 1 st  pronunciation section  362  of Kelly  360 , K is the constant and ε is the vowel. The 1 st  pronunciation section  362  of Kelly  360  is compared to the 1 st  pronunciation sections for words  366 - 374 . 
     In the compare, a similarity value for individual letters in the pronunciation section is determined. This is based on equation (1)  380  where for the first letter in the 1 st  pronunciation section, a similarity value is determined which may then be multiplied by a factor (e.g., a weighting factor). The factor is optional, but is reflective of the pronunciation language being used in the compare. 
     Determining a similarity value is repeated for other letters in the 1 st  pronunciation section of the words  366 - 374  being compared to the 1 st  pronunciation section  362  in Kelly  360 . Similarity values may then be multiplied by a respective factor. The similarity values determined for individual letters are added together for the similarity value to be used in equation (2)  390 . 
     Similarity values based on comparing the 2 nd  pronunciation section li  364  in Kelly  360  to the 2 nd  pronunciation sections of words  366 - 374  are determined as just discussed for the 1 st  pronunciation sections. Since Kelly  360  has two pronunciation sections and Catherine  374  has three pronunciation sections, the 3 rd  pronunciation section in Catherine  374  is ignored. 
     To determine the respective match values for individual words  366 - 374  using the compare, the similarity value for individual pronunciation sections is added together. The total value is then divided by the number of pronunciation sections in the converted word that is to be retrieved, as shown in equation (2)  390 . In the example, Kelly  370  has a match value of 1 whereas Karen has a match value of 0.6. The word having the highest match value that exceeds a threshold is selected, which in this case is Kelly  370 . The threshold may be 0.8, for example. 
     Since Kelly  360  matches with Kelly  370  in the list  342 , Kelly  370  is retrieved for display to the user. In some cases there may not be an exact match but a partial match when determining the contact to be retired. This variation may be due to how the user is pronouncing the word, such as with an accent or with a dialect that is peculiar to a specific region. As long as the match value exceeds the 0.8 threshold, there will be a high likelihood of a match with the spoken word. 
     Referring now to the languages supported by the database  350 , the languages may be selected, for example, based on employees within an organization that has offices in different countries. For an employee in an office in China, Chinese is the native language of the employee. In this case, the pattern of symbols for words in native language patterns  352  will be in Chinese. The primary language of the organization may be English, for example. In this case, the pattern of symbols for words in primary language patterns  354  will be in English. 
     The database  350  also supports other languages. In this case, the pattern of symbols for words in other language patterns  356  may be in Spanish, for example. Even though the database  350  includes three different language patterns, additional language patterns may be provided as needed, such as Japanese and Hindi, for example. 
     Once a match has been made, e.g., Kelly  370 , between the converted word Kelly  360  for one of the language patterns  352 - 356  in the database  350 , the processor  322  stores the language pattern providing the match in a custom language patterns section  358  in the database  350 . The custom language patterns section  358  advantageously allows this particular word to be more quickly retrieved a next time the user wants to retrieve the same word (e.g., a contact name) by speaking the word. 
     When performing a search for a word (e.g., a name of a contact), the processor  322  is configured to start the compare with the custom language patterns section  358  for a match with the converted word. For a word previously spoken by the user and matched to one of the language patterns  352 - 356  in the database  350 , the word is more efficiently retrieved from the custom language patterns section  358  since this section of the database has a limited number of entries as compared to the entries in the language patterns  352 - 356  that could potentially be searched. In response to there not being a match with any of the language patterns in the custom pattern section  358 , the processor  322  then continues the compare with the other language patterns  352 - 356  in the database. 
     Referring now to  FIG.  10   , a detailed flow diagram  400  for operating the computing device  300  will be discussed. Prior to the computing device  300  being used to retrieve a word  344  from the list  342 , the database  350  is created at Block  402 . The database  350  is created based on the processor  322  converting words  344  in the user&#39;s list of content  342  represented by alphabet characters to a pattern of pronunciation symbols  352 - 356  in different languages. 
     Individual words  344  in the list  342  may be represented by more than one language pattern. As noted above, the name Baron in English is ‘bær. n while the Japanese pronunciation is bΛr n. As another example, the name Peterman in English is ‘pi:t mæn. 
     To search for a word  344 , the user speaks into the microphone  310  and says a predetermined prefix, such as “call” or “email”, for example, followed by a word (e.g., the name of the contact)  344 . The processor  320  executes a voice-to-text conversion  324  at Block  404  to convert the word  344  spoken by the user to text, where the text is based on alphabet characters. 
     A determination is made at Block  406  as to whether the converted word in alphabet characters can be located in the list  342 . If the user pronounced the word correctly, which results in accurate conversion to alphabet characters by the voice-to-text conversion  324 , then the word will very likely be retrieved from the list  342 . Once retrieved, the word is displayed to the user at Block  408  for verification before the computing device  300  takes some action (e.g., initiating a telephone call or creating an email to the spoken name of a contact). 
     In response to an unsuccessful attempt to retrieve the word  344  in the list  342  based on using alphabet characters, the spoken word is converted to a pattern of symbols (e.g., pronunciation symbols) by the voice-to-pronunciation symbols conversion  326  at Block  410 . 
     Prior to comparing the pattern of symbols for the converted word to the different language patterns  352 - 356  for a match, custom language patterns  358  are searched first for a match. The different language patterns  352 - 356  are sequently searched for a match. That is, if a match is not found with the custom language patterns  358 , then the native language patterns  352  will be searched next. This process is repeated with the remaining primary language patterns  354  and the other language patterns  356  until a match is found. As noted above, there are no entries in the custom language patterns  358  until a word spoken by the user has been matched with one of the language patterns  352 - 356 . 
     A determination is made at Block  414  on if there is a match for a word previously spoken by the user with an entry in the custom language patterns  358 . If a match is found at Block  414 , then the matched pattern of symbols, as represented by Block  416 , is sent back to Block  406 . This causes the retrieved word corresponding to the matched pattern of symbols to be displayed at Block  408 . If a match is not found using the language patterns  358  at Block  414 , then the process continues to Block  418 . 
     At Block  418 , the converted word is compared to the pattern of symbols in native language patterns  352 . For a Chinese speaking user, the native language patterns  352  are in Chinese. A determination is made at Block  420  for a match with one of the native language patterns  352 . 
     If a match is found at Block  420 , then the matched pattern of symbols, as represented by Block  416 , is sent back to Block  406 . This causes the retrieved word corresponding to the matched pattern of symbols to be displayed at Block  408 . If this is a first time matching the spoken word with one of the native language patterns  352 , then the custom language patterns  358  is updated at Block  408  with the newly matched pattern. If a match is not found using the native language patterns  352 , then the process continues to Block  422 . 
     At Block  422 , the converted word is compared to the pattern of symbols in primary language patterns  354 . For an organization with offices in different countries, the primary language of the organization may be English. A determination is made at Block  424  for a match with one of the primary language patterns  354 . 
     If a match is found at Block  424 , then the matched pattern of symbols, as represented by Block  416 , is sent back to Block  406 . This causes the retrieved word corresponding to the matched pattern of pronunciation symbols to be displayed at Block  408 . If this is a first time matching the spoken word with one of the primary language patterns  354 , then the custom language patterns  358  is updated at Block  408  with the newly matched pattern. If a match is not found using the primary language patterns  354  at Block  424 , then the process continues to Block  426 . 
     At Block  426 , the converted word is compared to the pattern of pronunciation symbols in other language patterns  356 . For the organization with offices in different countries, other languages besides English and Chinese used within the organization may selected for the other language patterns  356 , such as Spanish or Japanese, for example. A determination is made at Block  428  on if there is a match with one of the other language patterns  356 . 
     If a match is found at Block  428 , then the matched pattern of symbols, as represented by Block  416 , is sent back to Block  406 . This causes the retrieved word corresponding to the matched pattern of pronunciation symbols to be displayed at Block  408 . If this is a first time matching the spoken word with one of the other language patterns  356 , then the language patterns  358  is updated at Block  408  with the newly matched pattern. If a match is not found using the other language patterns  356  at Block  428 , then the process continues to Block  430  which displays to the user that a word cannot be located. 
     Referring now to  FIG.  11   , a high-level flow diagram  450  for operating the computing device  300  will be discussed. From the start (Block  452 ), a word spoken by a user is converted into a pattern of pronunciation symbols at Block  454 . This is in response to an unsuccessful attempt to retrieve the word  344  in a list  342 . As noted above, the pattern of symbols provide a visual representation of speech sounds identifying the word in the list. 
     The pattern of symbols of the converted word are compared to a database  350  at Block  456 . The patterns in the database are in a format of symbols corresponding to the words in the list. Individual patterns used in the comparison has a match value assigned thereto, as discussed above for Kelly  360 , based on being compared to the pattern of symbols of the converted word. The word in the list corresponding to the pattern having the match value that is indicative of a match to the converted word is provided at Block  458 . The method ends at Block  460 . 
     Example implementations of methods, computing devices and computer-readable media in accordance with the present disclosure will now be provided. 
     The following paragraphs (M1) through (M12) describe examples of methods that may be implemented in accordance with the present disclosure. 
     (M1) A method include converting, based on rules, a word spoken by a user into a pattern of symbols in response to an unsuccessful attempt to retrieve the word in a list, with the pattern of symbols providing a visual representation of speech sounds identifying the word in the list. The pattern of symbols of the converted word are compared to a database of patterns, with the patterns in the database being in a format of symbols corresponding to the words in the list, and with each pattern used in the compare having a match value assigned thereto based on being compared to the pattern of symbols of the converted word. The word in the list corresponding to the pattern having the match value that is indicative of a match to the converted word is provided to the user. 
     (M2) A method as described in paragraph (M1), wherein the word spoken by the user is converted into alphabet characters before converting into the pattern of pronunciation symbols, and wherein the unsuccessful attempt to retrieve the word in the list is based on there not being a match between the alphabet characters representing the spoken word to alphabet characters representing the words in the list. 
     (M3) A method as described in any of paragraphs (M1) through (M2), further including ranking the match values assigned to the patterns used in the compare, and selecting the pattern having a highest ranked match value that exceeds a threshold. 
     (M4) A method as described in any of paragraphs (M1) through (M3), wherein the patterns in the database comprise a plurality of multi-lingual patterns, with each language pattern in the multi-lingual patterns being based on a particular language pronunciation of the word in the list, and with each word in the list being represented by more than one language pattern. 
     (M5) A method as described in any of paragraphs (M1) through (M4), wherein the comparing starts with a first one of language patterns having a particular language pronunciation, and in response to there not being a match, repeats the comparing with a second one of the language patterns having a different particular language pronunciation. 
     (M6) A method as described in any of paragraphs (M1) through (M5), wherein in response to there being a match with one of the language patterns having a particular language pronunciation, further includes adding the language pattern providing the match to a custom pattern section in the database, and for a next time a word is spoken by the user for retrieval, starting the compare using the language pattern in the custom pattern section. In response to there not being a match with the language pattern in the custom pattern section, continuing the compare with the other language patterns in the database. 
     (M7) A method as described in any of paragraphs (M1) through (M6), wherein performing the compare includes dividing the pattern of pronunciation symbols into pronunciation sections for the converted word, dividing the pattern of pronunciation symbols into pronunciation sections for each pattern in the database used in the compare, and comparing the pronunciation sections for the converted word to the corresponding pronunciation sections for each pattern used in the compare. 
     (M8) A method as described in any of paragraphs (M1) through (M7), wherein the matching value assigned to each pattern used in the compare is based on a respective similarity value assigned to each pronunciation section. 
     (M9) A method as described in any of paragraphs (M1) through (M8), wherein the matching value assigned to each pattern is determined by adding the respective similarity values assigned to the pronunciation sections for the pattern, and dividing the added respective similarity values by a number of the pronunciation sections in the converted word. 
     (M10) A method as described in any of paragraphs (M1) through (M9), wherein each pronunciation section comprises a plurality of letters, and wherein the respective similarity value assigned to each pronunciation section for the pattern used in the compare is based on the following: determining a similarity value for each letter in the pronunciation section for the pattern used in the compare, multiplying the similarity value for each letter in the pronunciation section by a respective weighting factor, and adding together the determined similarity value for each letter multiplied by the respective weighting factor to determine the similarity value assigned to each pronunciation section. 
     (M11) A method as described in any of paragraphs (M1) through (M10), wherein the rules for converting the word into a pattern of pronunciation symbols are based on an international phonetic alphabet (IPA). 
     (M12) A method as described in any of paragraphs (M1) through (M11), wherein retrieval of the word spoken is initiated by the user in response to the user speaking a predetermined word. 
     The following paragraphs (S1) through (S12) describe examples of computing devices that may be implemented in accordance with the present disclosure. 
     (S1) A computing device includes a memory and a processor configured to cooperate with the memory. The processor is configured to convert, based on rules, a word spoken by a user into a pattern of pronunciation symbols in response to an unsuccessful attempt to retrieve the word in a list. The pattern of pronunciation symbols provides a visual representation of speech sounds identifying the word in the list. The pattern of pronunciation symbols of the converted word are compared to a database of patterns, with the patterns in the database being in a format of pronunciation symbols corresponding to the words in the list. Each pattern used in the compare has a match value assigned thereto based on being compared to the pattern of pronunciation symbols of the converted word. The word in the list corresponding to the pattern having the match value that is indicative of a match to the converted word is provided to the user. 
     (S2) A computing device as described in paragraph (S1), wherein the word spoken by the user is converted into alphabet characters before converting into the pattern of pronunciation symbols, and wherein the unsuccessful attempt to retrieve the word in the list is based on there not being a match between the alphabet characters representing the spoken word to alphabet characters representing the words in the list. 
     (S3) A computing device as described in any of paragraphs (S1) through (S2), further including ranking the match values assigned to the patterns used in the compare, and selecting the pattern having a highest ranked match value that exceeds a threshold. 
     (S4) A computing device as described in any of paragraphs (S1) through (S3), wherein the patterns in the database comprise a plurality of multi-lingual patterns, with each language pattern in the multi-lingual patterns being based on a particular language pronunciation of the word in the list, and with each word in the list being represented by more than one language pattern. 
     (S5) A computing device as described in any of paragraphs (S1) through (S4), wherein the comparing starts with a first one of language patterns having a particular language pronunciation, and in response to there not being a match, repeats the comparing with a second one of the language patterns having a different particular language pronunciation. 
     (S6) A computing device as described in any of paragraphs (S1) through (S5), wherein in response to there being a match with one of the language patterns having a particular language pronunciation, further includes adding the language pattern providing the match to a custom pattern section in the database, and for a next time a word is spoken by the user for retrieval, starting the compare using the language pattern in the custom pattern section. In response to there not being a match with the language pattern in the custom pattern section, continuing the compare with the other language patterns in the database. 
     (S7) A computing device as described in any of paragraphs (S1) through (S6), wherein performing the compare includes dividing the pattern of pronunciation symbols into pronunciation sections for the converted word, dividing the pattern of pronunciation symbols into pronunciation sections for each pattern in the database used in the compare, and comparing the pronunciation sections for the converted word to the corresponding pronunciation sections for each pattern used in the compare. 
     (S8) A computing device as described in any of paragraphs (S1) through (S7), wherein the matching value assigned to each pattern used in the compare is based on a respective similarity value assigned to each pronunciation section. 
     (S9) A computing device as described in any of paragraphs (S1) through (S8), wherein the matching value assigned to each pattern is determined by adding the respective similarity values assigned to the pronunciation sections for the pattern, and dividing the added respective similarity values by a number of the pronunciation sections in the converted word. 
     (S10) A computing device as described in any of paragraphs (S1) through (S9), wherein each pronunciation section comprises a plurality of letters, and wherein the respective similarity value assigned to each pronunciation section for the pattern used in the compare is based on the following: determining a similarity value for each letter in the pronunciation section for the pattern used in the compare, multiplying the similarity value for each letter in the pronunciation section by a respective weighting factor, and adding together the determined similarity value for each letter multiplied by the respective weighting factor to determine the similarity value assigned to each pronunciation section. 
     (S11) A computing device as described in any of paragraphs (S1) through (S10), wherein the rules for converting the word into a pattern of pronunciation symbols are based on an international phonetic alphabet (IPA). 
     (S12) A computing device as described in any of paragraphs (S1) through (S11), wherein retrieval of the word spoken is initiated by the user in response to the user speaking a predetermined word. 
     The following paragraphs (CRM1) through (CRM12) describe examples of computer-readable media that may be implemented in accordance with the present disclosure. 
     (CRM1) A computer-readable medium for a computing device includes a plurality of computer executable instructions which, when executed, causes the computing device to convert, based on rules, a word spoken by a user into a pattern of pronunciation symbols in response to an unsuccessful attempt to retrieve the word in a list. The pattern of pronunciation symbols provides a visual representation of speech sounds identifying the word in the list. The pattern of pronunciation symbols of the converted word are compared to a database of patterns, with the patterns in the database being in a format of pronunciation symbols corresponding to the words in the list. Each pattern used in the compare has a match value assigned thereto based on being compared to the pattern of pronunciation symbols of the converted word. The word in the list corresponding to the pattern having the match value that is indicative of a match to the converted word is provided to the user. 
     (CRM2) A computer-readable medium as described in paragraph (CRM1), wherein the word spoken by the user is converted into alphabet characters before converting into the pattern of pronunciation symbols, and wherein the unsuccessful attempt to retrieve the word in the list is based on there not being a match between the alphabet characters representing the spoken word to alphabet characters representing the words in the list. 
     (CRM3) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM2), further including ranking the match values assigned to the patterns used in the compare, and selecting the pattern having a highest ranked match value that exceeds a threshold. 
     (CRM4) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM3), wherein the patterns in the database comprise a plurality of multi-lingual patterns, with each language pattern in the multi-lingual patterns being based on a particular language pronunciation of the word in the list, and with each word in the list being represented by more than one language pattern. 
     (CRMS) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM4), wherein the comparing starts with a first one of language patterns having a particular language pronunciation, and in response to there not being a match, repeats the comparing with a second one of the language patterns having a different particular language pronunciation. 
     (CRM6) A computer-readable medium as described in any of paragraphs (CRM1) through (CRMS), wherein in response to there being a match with one of the language patterns having a particular language pronunciation, further includes adding the language pattern providing the match to a custom pattern section in the database, and for a next time a word is spoken by the user for retrieval, starting the compare using the language pattern in the custom pattern section. In response to there not being a match with the language pattern in the custom pattern section, continuing the compare with the other language patterns in the database. 
     (CRM7) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM6), wherein performing the compare includes dividing the pattern of pronunciation symbols into pronunciation sections for the converted word, dividing the pattern of pronunciation symbols into pronunciation sections for each pattern in the database used in the compare, and comparing the pronunciation sections for the converted word to the corresponding pronunciation sections for each pattern used in the compare. 
     (CRM8) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM7), wherein the matching value assigned to each pattern used in the compare is based on a respective similarity value assigned to each pronunciation section. 
     (CRM9) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM8), wherein the matching value assigned to each pattern is determined by adding the respective similarity values assigned to the pronunciation sections for the pattern, and dividing the added respective similarity values by a number of the pronunciation sections in the converted word. 
     (CRM10) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM9), wherein each pronunciation section comprises a plurality of letters, and wherein the respective similarity value assigned to each pronunciation section for the pattern used in the compare is based on the following: determining a similarity value for each letter in the pronunciation section for the pattern used in the compare, multiplying the similarity value for each letter in the pronunciation section by a respective weighting factor, and adding together the determined similarity value for each letter multiplied by the respective weighting factor to determine the similarity value assigned to each pronunciation section. 
     (CRM11) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM10), wherein the rules for converting the word into a pattern of pronunciation symbols are based on an international phonetic alphabet (IPA). 
     (CRM12) A computer-readable medium as described in any of paragraphs (CRM1) through (CRM11), wherein retrieval of the word spoken is initiated by the user in response to the user speaking a predetermined word. 
     As will be appreciated by one of skill in the art upon reading the above disclosure, various aspects described herein may be embodied as a device, a method or a computer program product (e.g., a non-transitory computer-readable medium having computer executable instruction for performing the noted operations or steps). Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. 
     Furthermore, such aspects may take the form of a computer program product stored by one or more computer-readable storage media having computer-readable program code, or instructions, embodied in or on the storage media. Any suitable computer readable storage media may be utilized, including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, and/or any combination thereof. 
     Many modifications and other embodiments will come to the mind of one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is understood that the foregoing is not to be limited to the example embodiments, and that modifications and other embodiments are intended to be included within the scope of the appended claims.