[0001] The present disclosure relates to speech processing systems. In particular, the present disclosure relates to speech-based query systems in healthcare applications.

BACKGROUND OF THE DISCLOSURE
[0002] The background description includes response that may be useful in understanding the present invention. It is not an admission that any of the response provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Advancements in technology continue to transform customer service interactions. And, with AI today, those complex queries that are passed to human agents are no longer a burden. Automated customer service is not constrained by time zones or public holidays. This gives organizations the ability to deliver always-on customer service to resolve issues as soon as they arise. This means customers can have their inquiries resolved 24 hours a day, and don’t have to wait hours or even days for a response. This can greatly influence customer satisfaction and churn. In the Indian medical scenario too such kind of automated customer service is required because public sector hospitals face a lot of patients, especially during OPD hours. Due to lack of funds as well as resources, these hospitals are unable to provide customer support individually. Patients include people from various age groups, physically disabled, with diverse educative background. There can be a section of patients who can be illiterate while others may belong to rural areas.
[0004] There are various solutions to provide customer satisfaction in hospitals in India. Few of them use interfaces with Switch Case and Graphical User Interface (GUI). But they have some inherent complexities. In case of switch case, an entity can become irritated after listening to a long list of choices and sub-choices. It also becomes very time consuming as there is a limited domain for large number of questions. On the other hand, GUI is again very typical, and not entity friendly. For each speech query generated, a corresponding web page needs to be created which makes the whole system complicated and hard.
[0005] There is therefore a need in the art for an efficient automated system, which can be flexible for both literate as well as the illiterate and respond to their queries in their native language and process transactions (appointments) on the request of patients in regional language.

OBJECTS OF THE INVENTION
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] It is an object of the present disclosure to provide for a highly efficient speech enabled query system that can handle and manage data in a manner such that only relevant data or processed data is transmitted to devices that are operatively coupled with it.
[0008] It is an object of the present disclosure to provide for anentity friendly language interface to generate queries and obtain response pertaining to the said speech query.
[0009] It is an object of the present disclosure to provide for accurate response related to the speech query generated.
[00010] It is an object of the present disclosure to provide for a system to help in reducing human effort.
[00011] It is an object of the present disclosure to provide for efficient crowd management at the query section of a hospital thereby avoiding long queues.

SUMMARY
[00012] The present disclosure mainly relates to speech processing systems to enable queries in a specific language, in particular speech enabled query to obtain healthcare response.
[00013] This summary is provided to introduce simplified concepts of a speech enabled query system which are further described below in the Detailed Description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended for use in determining/limiting the scope of the claimed subject matter.
[00014] In an aspect, present disclosure elaborates upon a speech enabled query based system, said system may include a control unit configured to receive an audio signal pertaining to speech, from an entity; a processor operatively coupled with the control unit and with a memory, the memory storing instructions may be executable by the processor to: extract, from the received audio signal, first attributes of the audio signal, the first attributes indicative of speech patterns; extract, from the first attributes, second attributes indicative of a language of the speech; determine, from the extracted second attributes, a language of the speech enabled on comparison of the second attributes with a first dataset of second attributes and corresponding language identifiers; determine, from the extracted second attributes and the determined language, third attributes, the third attributes pertaining to a speech query of the user, based on comparison of the extracted second attributes with a second dataset of second attributes and corresponding queries; and determine, from the third attributes, a response in the language of the speech, wherein the response may be retrieved from a third dataset of responses for a speech query.
[00015] In an embodiment, the system may provide for a control unit, the control unit may include a microphone and the like to extract audio signal.
[00016] In an embodiment, the system may provide for the second attributes that may pertain to any or a combination of language identifiers of languages that may include Punjabi, Hindi, English, Bengali and the like.
[00017] In an embodiment, the system may provide for the speech query for a hospital enquiry service.
[00018] In an embodiment, the system may provide for the system to be operatively coupled to a healthcare server through a communication unit, wherein the communication unit may include any or a combination of cloud based network, WLAN and Bluetooth.
[00019] In an embodiment, a method may be provided for speech enabled query, the method may include a control unit configured to receive an audio signal pertaining to speech, from an entity; a processor operatively coupled with the control unit and with a memory, the memory storing instructions may be executable by the processor to perform the operations of: extracting, from the received audio signal, first attributes of the audio signal, the first attributes indicative of speech patterns; extracting, from the first attributes, second attributes indicative of a language of the speech; determining, from the extracted second attributes, a language of the speech enabled on comparison of the second attributes with a first dataset of second attributes and corresponding language identifiers; determining, from the extracted second attributes and the determined language, third attributes, the third attributes pertaining to a speech query of the user, based on comparison of the extracted second attributes with a second dataset of second attributes and corresponding queries; and determining, from the third attributes, a response in the language of the speech, wherein the response may be retrieved from a third dataset of responses for a speech query.
[00020] In an embodiment, the method may provide for the second attributes to pertain to any or a combination of language identifiers of languages such as Punjabi, Hindi, English, Bengali and the like.
[00021] In an embodiment, the method may provide for the speech enabled query method to be utilized in a hospital enquiry service.

BRIEF DESCRIPTION OF DRAWINGS
[00022] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[00023] FIG. 1 illustrates a network architecture of the proposed speech enabled query system in accordance with an embodiment of the present disclosure.
[00024] FIG. 2 illustrates an exemplary architecture of a processor associated with the speech enable in accordance with an embodiment of the present disclosure.
[00025] FIG. 3 illustrates a method illustrating the process generating speech enabled query in accordance with an exemplary embodiment of the present disclosure.
[00026] FIG. 4 illustrates an exemplary high-level flow diagram implementation of the speech enabled query system, in accordance with an embodiment of the present disclosure.
[00027] FIG. 5 illustrates a flow diagram associated with an example of the speech processing unit in accordance with an exemplary embodiment of the present disclosure.
[00028] FIG. 6 illustrates an exemplary representation of a working model of the proposed system in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[00029] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00030] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).
[00031] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[00032] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be readily apparent to persons skilled in the art. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed.
[00033] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[00034] The present disclosure mainly relates to speech processing systems to enable queries in a specific language, in particular speech enabled query to obtain healthcare response.
[00035] FIG. 1 illustrates an exemplary network architecture of a speech enabled query system 100 in accordance with an embodiment of the present disclosure.
[00036] As illustrated in FIG. 1, according to an aspect of the present disclosure a speech enabled query system 100 (also referred to as the system 100, hereinafter) can provide response related to a speech enabled speech query by using speech processing techniques. As illustrated, the control unit 102 can be communicatively coupled with one or more computing device 106-1, 106-2,.., 106-N (individually referred to as the computing device106 and collectively referred to as the computing devices 106 hereinafter) through a network 104. In an embodiment, the system 100 can be implemented using any or a combination of hardware components and software components such as a cloud, a server, a computing device, a network device, and the like. Further, the system 100 can interact with computing devices 106 through a website or an application that can reside in the computing devices 106. In an implementation, the system 100 can be accessed by website or application that can be configured with any operating system, including but not limited to, AndroidTM, iOSTM, Kai-OSTMand the like. Examples of the computing devices 106can include, but are not limited to, a computing device 106 associated with healthcare and hospital based assets, a smart phone, a portable computer, a personal digital assistant, a handheld simple phone and the like.
[00037] Further, the network 104 can be a wireless network, a wired network, a cloud or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, BLUETOOTH, MQTT Broker cloud, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the network 104 can either be a dedicated network or a shared network. The shared network can represent an association of the different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like. In an exemplary embodiment, the network 104 can be anHC-05 Bluetooth module which is an easy to use Bluetooth SPP (Serial Port Protocol) module, designed for transparent wireless serial connection setup.
[00038] In another embodiment, the network 104 herein can be a wired network used in case a computing device 106 is any of the older generation PC and can include a USB to Serial Converter(referred to as a USB serial adapter or RS232 adapter) to convert a USB signal to serial RS232 data signals.
[00039] According to various embodiments of the present disclosure, the system 100 can provide for an Artificial Intelligence (AI) based automatic speech detection and speech query generation by using signal processing analytics for healthcare applications, particularly for providing query services in native languages in hospitals. In an illustrative embodiment, the speech processing AI techniques can include, but not limited to, a Natural Language Processing Algorithm, said algorithm can be any or a combination of machine learning (referred to as ML hereinafter), deep learning (referred to as DL hereinafter), and natural language processing (referred to as NLP hereinafter). Said algorithm and other data or speech model involved in the use of said algorithm can be accessed from a database in the server, through an interface Natural language Interface to Database (referred to as NLIDB hereinafter).
[00040] In an aspect, the system 100 can receive a speech query pertaining to healthcare-based applications from the computing device106. In an embodiment, the system 100 can receive a batch (collection) of speech queries pertaining to healthcare applications and can consider one speech query from the batch of queries at a time for providing response. In an embodiment, the computing device 106 can be configured to receive an audio signal pertaining to speech query, from an entity through a microphone. The computing device 106 can extract the first attributes of indicative of speech patterns from the audio signal. For example, the audio signal can be related to a speech query enquiring about healthcare applications or any application pertaining to any healthcare organization such as private hospital, government hospital, pharmacy, and the like.
[00041] In an aspect, the system 100 can map the speech query with a speech model based on a particular dialect or language. The computing device 106 can be configured to extract, from the first attributes of the audio signal, a second attribute that can indicate a language of the speech and thereby can determine, from the extracted second attributes, a language of the speech by comparing the second attributes with a first dataset of second attributes and corresponding language identifiers. determine, from the extracted second attributes and the determined language, third attributes, the third attributes pertaining to a speech query of the entity, based on comparison of the extracted second attributes with a second dataset of second attributes and corresponding queries and thus create a third set of attributes which can include a third data set of possible responses for the query. From the third set of attributes, computing device 106 can determine a response in the language of the speech. In this way, the computing 106 can compare and map the speech query with related response. Speech processing techniques can be performed by applying lexicon, syntactic and semantic analysis and forwarding the analysis to structured speech query language (referred to as SQL, hereinafter) for providing required response to the speech query put in. In an aspect, a server can be operatively coupled with the system 100 that can store various speech models from which required response can be selected. For example, the entity can provide a speech query in Punjabi language asking for the availability of a doctor using the computing device 106 accessible therein and the system 100 can then retrieve response from the server pertaining to the time-table of a doctor in a particular day such that the response can be in the Punjabi language.
[00042] In yet another exemplary embodiment, an entity can make a speech query in a region not very close to a healthcare server. The entity herein can use the computing device 106 (for example a smart phone) connected with the network 104. For example, the entity can make a query about the doctor in Punjabi language that can be communicated via network 104 to the healthcare server. In an aspect, a server can be operatively coupled with the system 100 that can store various speech models from which required response can be selected and transmitted via said network 104 to the computing device 106 wherein the entity can avail the response. Thus, the entity can remotely send the speech query and obtain the required response without any hassle and thus provide for a smooth functioning of healthcare applications in hospital service management.
[00043] FIG. 2 illustrates an exemplary architecture of a processor 202 coupled with the speech enabled query system (100) in accordance with an embodiment of the present disclosure.
[00044] As illustrated, the control unit 102 can include one or more processor(s) 202. The one or more processor(s) 202 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 204 of the control unit 102. The memory 204 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[00045] The control unit102 can also include an interface(s) 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, transducers, actuators, and the like. The interface(s) 206 can facilitate communication of the control unit102 with various devices coupled to the control unit 102. The interface(s) 206 can also provide a communication pathway for one or more components of the control unit 102. Examples of such components include, but are not limited to, processing units 208 and database 210.In an exemplary embodiment, one interface but not limited to it, important to the invention can be the natural language interface for database (referred to as NLIDB hereinafter). Said interfaces can allow non-technical users to access and manage data stored in database 210 by any or a combination of asking and typing questions in natural language. For example, said interface can enable the entity to enter the speech query in the system 100 in natural language, not limited to Punjabi, English, Hindi, Kannada and Telugu. and can enable the entity to see the results of the entered speech query in the same language. In another exemplary embodiment, the speech data is stored in said database 210. Herein, said database 210, can be configured and developed through the interface 206that can sort queries of the user. Thus, an advantage of the proposed system can be to provide for an entity friendly language interface to generate queries and obtain response pertaining to the said speech query.
[00046] The processing units 208 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing units 208. The database 210 can include data that is either stored or generated because of functionalities implemented by any of the components of the processing units 208.
[00047] In an example, the processing units 208 can include an audio extraction unit 212, a channel creation unit 214, a speech processing unit 216, and other unit(s) 218. The other unit(s) 218 can implement functionalities that supplement applications or functions performed by the control unit 102 or the processing units 208.
[00048] In an embodiment, the audio extraction unit 212 of the control unit 102 can include processing units 218 that are responsible for extracting audio signal from an entity upon receiving acknowledgement from one or more processor(s) 202 and extract first attributes of the audio signal that indicate certain speech patterns. For example, the entity can use a microphone wherein he can provide his speech query which is further processed to extract the first attributes based on the speech pattern. Therefore, the advantage can be that the extraction unit can ensure that thesystem100 efficiently handle and manage data in a manner such that only relevant data or processed data can be transmitted to devices that are operatively coupled with it.
[00049] In an embodiment, the channel creation unit 214 of the control unit 102 can transmit the first attributes extracted from the audio extraction unit 212 to the healthcare server database 210 wherein responses mapped with the speech query can be stored. In an exemplary embodiment, the control unit 102 can provide for the channel creation unit 214 to can create communication channels between the processor 202, memory 204, database 210 and various mobile devices and network 104 that can include, but not limited to, an HC-05 Bluetooth module, designed for transparent wireless serial connection setup. In another exemplary embodiment, the control unit 102 can also provide for a channel creation unit 214 to create a communication pathway to transmit data to network 104 that can include, but not limited to USB to Serial Converter also referred to as a USB serial adapter or RS232 adapter such that it can convert a USB signal to serial RS232 data signals required in computing devices 106 that are older generation PCs. In yet another embodiment, the control unit 102 can provide for the channel creation unit 214to create data channels to communicate data to network 104 that can include, but not limited to Message Queuing Telemetry Transport Broker (interchangeably referred to as MQTT Broker hereinafter), wherein said MQTT Broker can transport messages between different computing devices 106.
[00050] In an embodiment, as illustrated in FIG. 2, the speech processing unit 216 of the control unit 102, can perform complex speech processing operations upon obtaining the first attributes of the speech query based on an executable set of instructions from one or more processors 202. The speech processing unit 216 can include components of artificial intelligence that can enable the control unit 102 to extract, from the first attributes, second attributes indicative of a language of the speech. The language of the speech can be based on comparison of the second attributes with a first dataset of pre-defined second attributes mapped to corresponding language identifiers. The speech processing unit 216 further determines, a set of third attributes based on comparison of the extracted second attributes mapped with corresponding queries. Finally from the third attributes, a response can be determined from the third dataset of possible responses for a speech query. Hence, the speech processing unit 216 can understand the speech query generated in natural language and can enable to provide the response related to the speech query. In an exemplary implementation, artificial intelligence can be implemented using techniques such as Machine Learning (referred to as ML hereinafter) that can focus on the development of programs and can access data and use the data to learn from it. Said MLcan provide the ability for the control unit 102 to learn automatically and improve the control unit 102 from experience without the necessity of being explicitly programmed. In another exemplary implementation, artificial intelligence can be implemented using deep learning (referred to as DL hereinafter) which is a subset of ML and can be used for big data processing for knowledge application, knowledge discovery, and knowledge-based prediction. The DL can be a network capable of learning from unstructured or unsupervised data. In yet another exemplary implementation, artificial intelligence can use techniques such as Natural Language Processing (referred to as NLP hereinafter) which can enable the control unit 102 to understand human speech. The NLP can make extensive use of phases of compiler such as syntax analysis and lexical analysis. For example, NLP= Text Processing + Machine Learning. The NLP can make use of any or a combination ofa set of symbols and a set of rules that govern a particular language. Symbols can be combined and used for broadcasting the response and rules can dominate the symbols in the language. NLP can herein teach machines through its ability to perform complex tasks in natural language no limited to dialogue generation, machine translation, summarization of text, sentiment analysis. The present disclosure provides for a speech enabled query system to help in reducing human effort. Hence, it can be seen that the proposed system 100 can remove the requirement of human efforts for responding to queries and thus can help in actively managing any crowd or rush at the query section of a hospital avoiding long queues. This can be an added advantage.
[00051] FIG. 3 illustrates a method illustrating the process of generating speech enabled query in accordance with an exemplary embodiment of the present disclosure.
[00052] In an aspect, the proposed method as elaborated hereunder can be described in general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method can also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[00053] The order in which the method as described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above described system.
[00054] In an aspect, a method for speech enabled query can include, at block302, receiving, at a control unit (102) coupled to a processor (202), an audio signal pertaining to speech, from an entity. Further at block 304, the method can include a step of extracting, at the processor (202), first attributes of the audio signal, the first attributes indicative of speech patterns.
[00055] In an aspect, the method can include at block 306, a step for extracting, at the processor (202), second attributes from the first attributes, said second attributes indicative of a language of the speech; and at block308, a step for determining, at the processor (202), from the extracted second attributes, a language of the speech enabled on comparison of the second attributes with a first dataset of second attributes and corresponding language identifiers can be provided.
[00056] In an aspect, the method can further include at block 310, a step for determining, at the processor 202, from the extracted second attributes and the determined language, third attributes, the third attributes pertaining to a speech query of the user, based on comparison of the extracted second attributes with a second dataset of second attributes and corresponding queries; and a block 312, for determining, at the processor 202, from the third attributes, a response in the language of the speech, wherein the response can be retrieved from a third dataset of possible responses for a speech query.
[00057] FIG. 4 illustrates an exemplary high-level flow diagram implementation of the proposed speech enabled query system, in accordance with an embodiment of the present disclosure.
[00058] In an embodiment, with respect to FIG. 4, the high level flow diagram can involve a series of steps wherein at block 402, entity enters the speech query in Punjabi language. At block 404, speech query is divided in tokens and the tokens are stored in an array. At block 406, keyword stored in the database, and at block 408, the token is compared with each keyword. At block 410, token is matched with the keyword stored in the database. If token is matched, then upon determining yes, at block 412, the keyword is analysed and at block 414, the keyword is adjusted according to the position in the speech query. At block 416, all the parts of the speech query according to syntax are joined, and at block 418, the speech query is executed and the results are displayed. At block 410, token is matched with the keyword stored in the database. If token is not matched, then upon determining no, at block 420, next token available in the array is taken. If next token is available upon determining yes, at block 422, the next token from the array is fetched, which goes to block 408, and the token is compared with each keyword and the process is repeated again. At block 420, next token available in the array. If not available, then upon determining no, at block 414, the keyword is adjusted according to the position in the speech query. At block 416, all the parts of the speech query are joined according to syntax, and at block 418, the speech query is executed and theresults are displayed.
[00059] FIG. 5 illustrates a flow diagram associated with an example of the speech processing unit 216in accordance with an exemplary embodiment of the present disclosure.
[00060] In an embodiment, the computing unit 102 can provide for the speech processing unit 208, to perform multiple speech processing upon receiving a set of executable instructions from the processor 202. The processing units 208 coupled to the database 210 wherein NLIDB can be stored. The NLIDB can acknowledge speech query and can attempt to comprehend it by applying lexicon, syntactic and semantic analysis and then can process the changes into SQL. As illustrated in FIG. 5, the steps involved in the NLIDB can be such that at block 502, entity can input speech query in natural language, said speech query can be sent for Morphological analysis at block 504, wherein it can be further sent for processing at block 506 for syntactic analysis, at block 508, semantic analysis. Further it can lead to generation of SQL speech query at block 510 and processing the speech query on database 210 at block 512. Upon performing said steps of NLIDB, the result can be obtained.
[00061] FIG. 6 illustrates an exemplary representation of a working model of the proposed system 100 in accordance with an embodiment of the present disclosure.
[00062] As illustrated in the example, the computing device 106 can include an Android App for speech processing 602 which can extract the audio signal and can determine the first attributes related to speech patterns and can map with a corresponding set of language identifiers which can be the second set of attributes. The second set of attributes are transmitted via the network 104 that can be a wireless network, a wired network, a cloud or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, BLUETOOTH, MQTT Broker cloud, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the network 104 can either be a dedicated network or a shared network. In an exemplary implementation, the network 104 can be an HC-05 Bluetooth module 602. The HC-05 Bluetooth Module 602 can be used in any or a combination of Master and Slave configuration. This serial port Bluetooth module can be fully qualified with a protocol Bluetooth V2.0 and Enhanced Data Rate (EDR) having 3Mbps Modulation complete with 2.4 GHz radio transceiver and baseband. Said Bluetooth module 602 can use CSR Bluecore 04 which can be an external single chip Bluetooth system with CMOS technology and with Adaptive Frequency Hopping Feature.
[00063] In another implementation, the network 104 herein can be a wired network used in case the computing device 106 is any of the older generation PC and can include a USB to Serial Converter 604 (referred to as a USB serial adapter or RS232 adapter) to convert a USB signal to serial RS232 data signals. At the server 614, the second attributes in turn can generate the third set of attributes which map to the responses corresponding to the query of the entity by the use of Natural Language Processing Algorithm 610 operatively coupled to the database 612 wherein the responses mapped to the queries can be stored. The responses can be then transmitted to the computing devices 106 via network 104 that can include any or a combination of cloud based MQTT Broker 616, the Bluetooth HC module 604 and the USB to serial converter 606.
[00064] In yet another example, the MQTT Broker, 616 herein may be an ISO standard, OASIS, lightweight, publish subscribe protocol that can transport message between devices. The protocol can usually run over TCP/IP. However, any network protocol that can provide lossless, ordered as well as bi-directional connections can support MQTT broker. The MQTT protocol can define two types of network entities: a message broker and a number of clients. An MQTT broker can be a server that receives all messages from the clients and then routes the messages to the appropriate destination clients. An MQTT client can be any device including but not limited to a micro controller, a full-fledged server and the like that can run on MQTT library and can connect to an MQTT broker over said network 104.
[00065] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with response and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[00066] The present disclosure provides for a speech enabled query system that efficiently handles and manages data in a manner such that only relevant data or processed data is transmitted to devices that are operatively coupled with it.
[00067] The present disclosure provides for a speech enabled query system that can provide for an entity friendly language interface to generate queries and obtain response pertaining to the speech query.
[00068] The present disclosure provides for a speech enabled query system that can provide accurate response related to the speech query generated.
[00069] The present disclosure provides for a speech enabled query system to help in reducing human effort.
[00070] The present disclosure provides for a system that actively manages any crowd or rush at the query section of a hospital avoiding long queues.

Claims:1. A speech enabled query based system, said system comprising:
a control unit (102) configured to receive an audio signal pertaining to speech, from an entity;
a processor (202) operatively coupled with the control unit (102) and with a memory (204), the memory (204) storing instructions executable by the processor (202) to:
extract, from the received audio signal, first attributes of the audio signal, the first attributes indicative of speech patterns;
extract, from the first attributes, second attributes indicative of a language of the speech;
determine, from the extracted second attributes, a language of the speech enabled on comparison of the second attributes with a first dataset comprising of second attributes and corresponding language identifiers;
determine, from the extracted second attributes and the determined language, third attributes, said third attributes pertaining to a speech query of the entity, based on comparison of the extracted second attributes with a second dataset comprising of the second attributes and corresponding queries; and
determine, from the third attributes, a response in the language of the speech, wherein the response is retrieved from a third dataset of responses for a speech query.
2. The system as claimed in claim 1, wherein the control unit(102) receives the audio signal through a microphone.
3. The system as claimed in claim 1, wherein the second attributes pertain to language identifiers of any or a combination of Punjabi, Hindi, English, Bengali languages.
4. The system as claimed in claim1, wherein the speech enabled query system is a hospital query service.
5. The system as claimed in claim 1, wherein the system is operatively coupled to a healthcare server through a communication unit (214), wherein the communication unit (214)is any or a combination of cloud based network, WLAN, optical fibre and Bluetooth.
6. A method for facilitating a speech enabled query, said method comprising the steps of:
receiving, at a control unit (102) coupled to a processor (202), an audio signal pertaining to speech, from an entity;
extracting, at the processor (102), first attributes of the audio signal, the first attributes indicative of speech patterns;
extracting, at the processor (102), second attributes from the first attributes, said second attributes indicative of a language of the speech;
determining, at the processor (102), from the extracted second attributes, a language of the speech enabled on comparison of the second attributes with a first dataset of second attributes and corresponding language identifiers;
determining, at the processor (102), from the extracted second attributes and the determined language, third attributes, the third attributes pertaining to a speech query of the user, based on comparison of the extracted second attributes with a second dataset of second attributes and corresponding queries; and
determining, at the processor (102), from the third attributes, a response in the language of the speech, wherein the response is retrieved from a third dataset of responses for a speech query.
7. The method as claimed in claim 7, wherein the second attributes pertain to language identifiers of any or a combination of Punjabi, Hindi, English, Bengali languages.

8. The method as claimed in claim 7, wherein the speech enable query method is utilized in a hospital query service.