System and method for cross-lingual time-synchronized translations

Abstract

The present disclosure relates to a method and system for cross-lingual translation. The method, performed by a server system, includes receiving an input content in a source language. In addition, the method includes translating the input content in a target language based, at least in part, on implementation of a translation model. Further, the method includes performing time-synchronization on the translated input content based, at least in part, on implementation of an integer linear programming (ILP) model.

Description

SYSTEM AND METHOD FOR CROSS-LINGUAL TIME-SYNCHRONIZED TRANSLATIONS TECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to the field of machine translation. More particularly, embodiments of the present disclosure relate to time synchronization of translated content for cross-lingual systems. BACKGROUND

[0002] In today's digital age, the advancements in Natural Language Processing (NLP) have brought about a significant transformation in the way machines understand and respond to human input data. With the integration of Machine Learning and Artificial Intelligence, intelligent systems today are capable of cross-lingual learning and communication. For example, NLP is applied in a variety of real-world situations, such as answering user-generated questions, translating responses into user-specific languages, assisting travellers around the globe in understanding foreign languages by translating foreign speech into their native tongues, processing and translating speech data in real-time while simultaneously displaying the output into multilingual subtitles during a video streaming service like YouTube, etc.

[0003] However, the accurate and effective translation of speech or verbal utterances in real-time scenarios demands precision and time-synchronization. Therefore, recently there has been an advent in utilization of various cross-lingual time-synchronized machine translation systems. The cross-lingual learning process typically involves a pipeline for tokenizing the text, translating it using rule-based, statistical, or neural machine translation methods, and then detokenizing the output. Additionally, such systems may utilize various machine Learning techniques for achieving optimal results. However, the entire process is resource-intensive and requires servers with high computational capabilities for the execution of the processes involved, thereby reducing the efficiency of the system, which is a significant problem for real- time applications. Moreover, such traditional approaches to these problems require human intervention or inefficient computational methods. Also, the conventional systems struggle to scale across multiple languages, especially when considering time synchronization as different languages have different speech rates, and managing this disparity in a multi-lingual setting is a significant challenge.

[0004] Several patents related to Cross-Lingual understanding or learning, time synchronization, and the use of Machine Learning techniques such as ILP and GPT models already exist. US Patent No. 7,822,627 describes Statistical Machine Translation (SMT), and US patent No. 9,886,330 titled “Double processing offloading to additional and central processing units” describes technologies around using neural networks for machine translation. These technologies form the foundation for any cross-lingual time-sync algorithm. US Patent No. 10,555,103 titled “Translating incoming and outgoing messages” disclosing real-time translation and US Patent No. 10,331,116 titled "Real-time translation with device-specific adaptation" are some more prominent examples. Similarly, US Patent No 10,557,153 titled “Methods and systems for aligning comparable sentences for parallel corpora in different languages” describes the alignment of sentences across different languages.

[0005] Additionally, research papers such as "Global Inference for Sentence Compression an Integer Linear Programming Approach" by Clarke & Lapata, 2008 describes the use of ILP in various NLP tasks such as part-of-speech tagging and semantic role labeling while a research paper titled "Language Models are Few-Shot Learners" (2020) discloses technical details regarding development of several versions of GPT (GPT-1, GPT-2, GPT-3) by OpenAI.

[0006] However, these techniques still fall short when it comes to addressing the issue of time synchronization in real-time applications. For the same reason, these cross-lingual systems are generally not able to synchronize translated content with the timing of the original content while maintaining semantic consistency across multiple languages. Additionally, such systems are not trained for a wide variety of languages.

[0007] Therefore, there exists an imperative need in the art for a cross-lingual time synchronized machine translation system that not only allows accurate time synchronization but is also capable of maintaining semantic consistency and facilitating scalability across multiple languages apart from having the capability of effectively dealing with the problem of inefficient utilization of computational resources. OBJECTS OF THE INVENTION

[0008] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0009] It is an object of the present disclosure to provide a cross-lingual translation system that automates the process of time synchronization, delivering accurate time synchronized output in real-time without the need of manual intervention.

[0010] It is another object of the present disclosure to provide a cross-lingual translation system that ensures semantic accuracy of the time-aligned translations, thereby maintaining a semantic intent of the original content even when the translation is time-adjusted.

[0011] It is yet another object of the present disclosure to provide a cross-lingual translation system that allows for scalability across a plurality of languages. SUMMARY

[0012] In an aspect, a method for cross-lingual translation is disclosed. The method includes receiving, by a server system, an input content in a source language. In addition, the method includes translating, by the server system, the input content in a target language based, at least in part, on implementation of a translation model. Further, the method includes performing, by the server system, time-synchronization on the translated input content based, at least in part, on implementation of an integer linear programming (ILP) model.

[0013] In an embodiment, the input content is received in at least one format.

[0014] In an embodiment, the at least one format includes text, audio, video, animation, or combinations thereof.

[0015] In an embodiment, the translation model includes at least one of Generative Pre- trained Transformer (GPT) model, Dynamic Time Warping (DTW) model, Large Language Model (LLM), Recurrent Neural Network (RNN) model, Sequence-to-Sequence model, or combinations thereof.

[0016] In an embodiment, the time-synchronization is performed based on at least one parameter. The at least one parameter includes one of start time, end time, and ideal segment length.

[0017] In another aspect, a server system for cross-lingual translation is disclosed. The server system includes a processor, a memory, and a communication interface coupled to the processor and the memory. The memory stores instructions, which when executed by the processor, causes the server system to receive an input content in a source language. The server system is then caused to translate the input content in a target language based, at least in part, on implementation of a translation model. In addition, the server system is caused to perform time-synchronization on the translated input content based, at least in part, on implementation of an integer linear programming (ILP) model. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Also, the embodiments shown in the figures are not to be construed as limiting the disclosure, but the possible variants of the method and system according to the disclosure are illustrated herein to highlight the advantages of the disclosure. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components.

[0019] FIGURE 1 illustrates an exemplary block diagram of a server system for cross- lingual time synchronized translations, in accordance with an exemplary embodiment of the present disclosure.

[0020] FIGURE 2 illustrates an exemplary method flow diagram indicating the process for time synchronizing cross-lingual translations, in accordance with an exemplary embodiment of the present disclosure.

[0021] The foregoing shall be more apparent from the following more detailed description of the disclosure. DESCRIPTION

[0022] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.

[0023] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0024] 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, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.

[0025] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure.

[0026] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.

[0027] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor.

[0028] As used herein, “a computing unit”, “a user equipment”, “a user device”, “a smart- user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”,“a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.

[0029] As used herein, “storage unit” or “memory unit” refers to a machine or computer- readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.

[0030] As discussed in the background section, the current known solutions for a cross- lingual systems, have several shortcomings such as time synchronization issues, resulting in disparities between translated and original content timing. Further, resource-intensive processes for the purpose of translation leads to an inefficient utilization of computational resources and further unsynchronized translations. Additionally, while trying to match the timing of the original and translated content, semantic accuracy might be compromised in existing systems. In conventional methods and systems, human intervention was necessitated to ensure semantic consistency. Moreover, existing systems often struggled to scale across multiple languages, especially when considering time synchronization. Different languages have different speech rates, and managing this disparity in a multi-lingual setting is a significant challenge.

[0031] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by utilizing an efficient, automated method for real-time, cross-lingual time-synchronization using Integer Linear Programming (ILP) and Generative Pre-trained Transformers (GPT) based semantic merging. Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0032] FIGURE 1 illustrates an exemplary block diagram

[0100] of a server system

[0110] for cross-lingual time synchronized translations, in accordance with an exemplary embodiment of the present disclosure. The block diagram

[0100] includes a computing unit

[0105] , a communication medium

[0115] , and the server system

[0110] .

[0033] The server system

[0110] is adapted to receive an input content in a source language from the computing unit

[0105] through the communication medium

[0115] for efficient and accurate cross-lingual time synchronization. The server system

[0110] comprises at least one processing unit

[0102] and at least one storage unit

[0104] . Also, all of the components / units depicted in the block diagram

[0100] are assumed to be connected to each other unless otherwise indicated below. Also, in Figure 1, only a few units are shown. However, the block diagram

[0100] may comprise multiple such units or the block diagram

[0100] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the server system

[0110] may be present in a user device (e.g., the computing unit

[0105] ) to implement the features of the present invention. The server system

[0110] may be a part of the user device / or may be independent of but in communication with the user device. In another implementation, the server system

[0110] may be a remote server. In yet another implementation, the server system

[0110] may reside partly in the server and partly in the user device.

[0034] The server system

[0110] includes a translation model

[0120] that is configured for efficient and accurate cross-lingual time synchronization using the combined power of various sub models such as, Integer Linear Programming (ILP) and Generative Pretrained Transformer (GPT) models, and / or other programming models of the current disclosure, with the help of the interconnection between the components / units of the block diagram

[0100] . Examples of other programming models include but are not limited to Dynamic Time Warping (DTW), Recurrent Neural Networks (RNN), Sequence-to-Sequence Models and other Custom Training Regimes.

[0035] In order to provide a time synchronized translation, the processing unit

[0102] is configured to process the programming instructions of the translation model

[0120] so as to process a received original content (i.e., the input content), typically in the form of speech or text, which needs to be translated and time-aligned. The input content is then tokenized to break it down into smaller units that can be processed by the subsequent stages, where specific steps for performing translations as well as for aligning the translations in real time is performed.

[0036] The input content is received in at least one format. The at least one format comprises text, audio, video, animation, or combinations thereof. In an example, the input content is a text document. In another example, the input content is an audio file in a source language (e.g., French). In yet another example, the input content is a video file in a source language (e.g., Spanish).

[0037] The storage unit

[0104] is configured to store translation related information such as original content, translations, time alignment parameters, time synchronization parameters, various NLP and / or processing models, and the like.

[0038] Further, the disclosed framework includes all the possibilities of data pick up to prepare time synchronized translations. The input content may be received directly in the form of text and / or speech, some translation is performed from local databases and some data is translated to using GPT’s master reference data.

[0039] The processing unit

[0102] is then configured to translate the input content in a target language based, at least in part, on implementation of the translation model

[0120] . The translation model comprises at least one of Generative Pre-trained Transformer (GPT) model, Dynamic Time Warping (DTW) model, Large Language Model (LLM), Recurrent Neural Network (RNN) model, Sequence-to-Sequence model, or combinations thereof.

[0040] The processing unit

[0102] is further configured to perform time-synchronization on the translated input content based, at least in part, on implementation of an integer linear programming (ILP) model

[0125] . The time-synchronization is performed based on at least one parameter. The at least one parameter comprises one of start time, end time, and ideal segment length. The process of performing time-synchronization is explained in detail below in FIGURE 2.

[0041] FIGURE 2 illustrates an exemplary method flow diagram

[0200] indicating the process for time synchronizing cross-lingual translations, in accordance with an exemplary embodiment of the present disclosure.

[0042] In an implementation, the method

[0200] is performed by the server system

[0110] . As shown in Figure 2, the method

[0200] starts at step

[0202] .

[0043] At step

[0204] , the method

[0200] as disclosed by the present disclosure comprises receiving, by the server system

[0110] , the input content in a source language. The input content represents the content that needs to be translated from the source language to a target language.

[0044] Next, at step

[0206] , the method

[0200] as disclosed by the present disclosure comprises translating, by the server system

[0110] , the input content in the target languagebased, at least in part, on implementation of the translation model

[0120] . The translation model

[0120] may include machine learning models, such as for example, GPT models for performing the translation. Particularly, the GPT, a powerful language model that has been pre-trained on large amounts of text data, uses deep learning techniques to understand the nuances of language so as to accurately translate content from one language to another. The advantage of using GPT for translation is that it can handle complex sentence structures and idiomatic expressions that are common in natural language. This ensures that the translated content is not only accurate but also natural sounding, thereby making it easier for the recipient to understand.

[0045] At next step

[0208] , the method

[0200] comprises performing, by the server system

[0110] , time-synchronization on the translated input content based, at least in part, on implementation of the integer linear programming (ILP) model

[0125] . The ILP model

[0125] is utilized for the purpose of achieving cross-lingual time synchronization. Particularly, the ILP model

[0125] is adapted to structure translations in a manner that closely aligns with the timing of the original speech or text. The ILP model

[0125] utilizes an ILP algorithm that optimizes timing while considering constraints such as start times, end times, and ideal segment lengths. In other embodiments, any known programming model may be utilized for the purpose of time synchronization.

[0046] Thereafter, the method

[0200] terminates at step

[0210] .

[0047] In an embodiment, the server system

[0110] is configured to further process the translated input content that require further synchronization, for example, by reducing the length of translated input content while maintaining semantic accuracy. This step ensures hyper-accurate time synchronization.

[0048] While the present disclosure leverages the translation model

[0120] (e.g., GPT) and the ILP model

[0125] , alternative embodiments may include different optimization algorithms for time alignment, other language models for semantic merging, or even custom training regimes that incentivize timing synchronization. Exemplary algorithms are mentioned below:

[0049] Dynamic Time Warping (DTW) model: DTW model may implement an algorithm for measuring similarity between two temporal sequences that may vary in speed. This approach could be useful in cases where the original speech or text is not consistent in terms of timing.

[0050] Other Optimization models: Genetic models, Particle Swarm Optimization models or even simpler linear programming models could be employed for a similar purpose. Thesemodels could be useful in cases where the ILP model

[0125] is not suitable due to the complexity of the scheduling problem. Alternative Pretrained Language Models: Other language models like BERT or RoBERTa could also be adapted for similar tasks. These models may have different strengths and weaknesses compared to the GPT model, making them more suitable for certain types of translation tasks.

[0051] Recurrent Neural Networks (RNN) models: RNNs could be employed to manage sequence and timing information inherent in translation tasks. Such models could be useful in cases where timing is a critical factor in understanding the conversation.

[0052] Sequence-to-Sequence models: Sequence-to-sequence models can be adapted with custom loss functions or auxiliary tasks to encourage models to match output timing with input timing. Such models could be useful in cases where timing is a critical factor in understanding the conversation and where the ILP model

[0125] is not suitable due to the complexity of the scheduling problem.

[0053] In one embodiment, the ILP model

[0125] implements the ILP algorithm to perform time-synchronization on the translated input content. The translated input content represents the output content after the translation. In an embodiment, the server system

[0110] is configured to display the translated input content (i.e., the output content) on a display of the computing unit

[0105] .

[0054] In one embodiment, the ILP algorithm, “calculate_optimal_segments”, is configured to optimize the time synchronization of the translated input content. The ILP algorithm employs Integer Linear Programming (ILP) to determine the optimal start times and scalar values for each segment of translated text. The scalar values indicate how close each segment's length is to its ideal length. The ILP algorithm is as follows: def calculate_optimal_segments( starts, ideal_lengths, end, max_delta: float = MAX_START_DELTA ):

[0055] Objective function explanation:

[0056] Scalars define how close to the ideal length each segment is. A final scalar value of 1 means that the segment will be able to be its ideal length. A final scalar value of 0 means that the segment will be as short as far away from the ideal length as possible.

[0057] In the ILP algorithm, 3 variables are defined: new_starts, scalars, and d; wherein new_starts is the new start time for each segment, scalars is the scalar value for each segment, and d is the difference between each consecutive scalar value.

[0058] The objective function is MAX FUNC of a weighted sum of 3 terms:

[0059] 1. The minimum scalar value. This term is weighted the most heavily. This term ensures that the segments are as close to their ideal length as possible. Maximizing the minimum scalar value ensures that every segment is as close to their ideal length as possible.

[0060] 2. The average of the negation of absolute value of the difference between the new start times and the old. This term is weighted in the middle. This term ensures that the new start times are as close to the old start times as possible.

[0061] 3. The sum of the negation of absolute differences between consecutive scalars. This term is weighted the least. This term ensures that consecutive scalars are as close to each other as possible, so that there are no abrupt changes between two consecutive segments. ''' n = len(starts) end = max(end, starts[-1] + EPSILON) new_starts = cp.Variable(n) # type: ignore scalars = cp.Variable(n) # type: ignore d = cp.Variable(n-1) # type: ignore # TODO: weight each element of the differences `d` differently if the dialogues are far / near. # diff_starts = [starts[i+1] - starts[i] for i in range(n-1)] # sum_diff_starts = sum(diff_starts) # diff_starts = [1 - i / sum_diff_starts for i in diff_starts] # Set up constraints constraints = [ new_starts[0] >= 0, # type: ignore new_starts[n - 1] + scalars[n - 1] * ideal_lengths[n - 1] + EPSILON <= end, scalars <= 1, # type: ignore scalars >= 0, # type: ignore# this ensures that d_i is atleast the absolute value of the difference between scalars[i] and scalars[i+1]. # although this is not a strict equality, it is sufficient for our purposes because we are maximizing the sum # of the negation of absolute differences between consecutive scalars (see objective function explanation above). # and so, the objective function will ensure that d_i is as close to the absolute value of the difference between # scalars[i] and scalars[i+1] as possible. d >= 0, # differences should be non-negative d >= scalars[:-1] - scalars[1:], # differences are greater than or equal to (scalars[i] - scalars[i+1]) d >= scalars[1:] - scalars[:-1] # differences are greater than or equal to (scalars[i+1] - scalars[i]) ] # TODO: think more regarding weighting # constraints.extend( # d >= diff_starts[i] * (scalars[i] - scalars[i+1]) for i in range(n-1) # ) # constraints.extend( # d >= diff_starts[i] * (scalars[i+1] - scalars[i]) for i in range(n-1) # ) constraints.extend( new_starts[i] + scalars[i] * ideal_lengths[i] + EPSILON <= new_starts[i + 1] for i in range(n - 1) )constraints.extend( cp.abs(new_starts[i] - starts[i]) <= max_delta for i in range(n) ) # Define the multi-objective problem # Every term in the objective function is normalized between 0 and 1. objective = cp.Maximize(WEIGHT_MIN_SCALARS * (cp.min(scalars) + cp.sum(scalars) / n) / 2 - # needs high weight WEIGHT_MIN_STARTS * cp.sum(cp.abs(new_starts - starts)) / (max_delta * n) - WEIGHT_MIN_DIFFS * (cp.sum(d) / (n-1))) problem = cp.Problem(objective, constraints) # type: ignore result = problem.solve(verbose=False) # type: ignore new_starts = list(new_starts.value) new_starts[0] = max(0, new_starts[0]) new_lengths = list(scalars.value * ideal_lengths) speedups = [ ideal_length / actual_length for ideal_length, actual_length in zip(ideal_lengths, new_lengths) ] print(f""" Old starts: {list(starts)} Ideal lengths: {list(ideal_lengths)} end time: {end} New starts: {list(new_starts)} New lengths: {list(new_lengths)} Speedups: {list(speedups)}Optimal speedups: {[round(x, 3) for x in speedups]} """) return new starts, new lengths, speedups

[0062] In one implementation, given a sequence of ^ input language dialogues with respective durations ^1, ^2, …, ^^ and their corresponding translations, the objective of the ILP model

[0125] is to synchronize output dialogues (i.e., translations) with the input language dialogues in the following manner: 1. Start-to-End Alignment: The sequence of translated dialogues should be aligned with the input dialogues in such a way that the translated sequence starts simultaneously with the first input dialogue and concludes within the timeframe of the last input dialogue. 2. Minimal Playback Rate Variation: The playback rates between consecutive translated dialogues should be as consistent as possible. This ensures a natural and smooth listening experience, with minimal variation in playback speed between successive output dialogues. 3. Length Preservation: The ILP algorithm aims to keep the ratio of the original length of each output dialogue to its final adjusted length as close to 1 as possible. This means that while slight adjustments in the duration of the output dialogues are permissible, the ILP algorithm strives to maintain their original length as much as possible.

[0063] As is evident from the above, the present disclosure provides a technically advanced solution for time synchronized translation in a semantic accurate manner.

[0064] In view of the above, the present disclosure provides a unique solution to the challenges of real-time, cross-lingual translation and synchronization. By integrating the ILP model

[0125] and the translation model

[0120] , the server system

[0110] provides efficient, accurate, and scalable cross-lingual translation solution with applications across multiple sectors. Particularly, the cross-lingual system as disclosed facilitates translations in a plethora of languages that can be tailored to meet the specific needs of each business.

[0065] The proposed invention offers several distinct advantages over existing technologies:

[0066] 1. Efficient Time Synchronization: By leveraging the ILP model

[0125] , the server system

[0110] can efficiently align the timing of the translated input content with the inputcontent. This is particularly valuable in real-time or near-real-time settings such as live broadcasts or conferences.

[0067] 2. Improved Semantic Accuracy: By leveraging the translation model

[0120] (e.g., GPT-based semantic merging), the server system

[0110] ensures that the translated input content maintains the semantic intent of the input content, even when the translation is time-adjusted.

[0068] 3. Better Utilization of Computational Resources: The application of the ILP model

[0125] for time alignment provides an optimization method that is less computationally intensive than many conventional techniques. This efficient utilization of resources is crucial for real- time applications and large-scale deployments.

[0069] 4. Scalability Across Multiple Languages: The server system

[0110] allows for scalability across multiple languages, as the translation model

[0120] can handle semantic merging in a wide variety of languages, and the ILP model

[0125] can adjust the timing of the translations irrespective of the specific language.

[0070] 5. Increased Accessibility: The server system

[0110] can make a variety of media, events, and services more accessible to people who speak different languages. Real-time, semantically accurate translation and synchronization can help overcome language barriers in international conferences, online content, customer service, and emergency services, among others.

[0071] 6. Versatility of Application: The cross-lingual translation approach can be integrated into various services, from real-time translation applications to live broadcast subtitling systems, making it a versatile solution in the realm of machine translation.

[0072] 7. Automated Process: The present invention automates the process of cross- lingual time-synchronization, thereby reducing the need for manual intervention and increasing the speed and efficiency of translation tasks.

[0073] Accordingly, it may be seen that the server system

[0110] of the present disclosure may act as an essential tool for any business that may need a time-synchronized translation. The present invention presents a solution for real-time cross-lingual time synchronization using ILP and GPT-based semantic merging. The present invention offers several advantages over existing technologies by providing efficient time synchronization while maintaining semantic accuracy and scalability across multiple languages. This approach has immediate applications in various sectors such as language translation services, multimedia services, international conferences, social media platforms, customer service, educational tools, emergency services, governmental use, and AI-powered assistants. With its automated process and versatileapplication potential, the present invention can revolutionize real-time cross-lingual communication in the globally connected world.

[0074] In addition to time-synchronization, the server system

[0110] improves efficiency, reduces costs, and improves quality control. By automating the translation process, businesses can save time and resources while also ensuring that all requests are handled accurately and efficiently.

[0075] While considerable emphasis has been placed herein on the disclosed embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.

Claims

CLAIMS I / We claim:

1. A method for cross-lingual translation, comprising: receiving, by a server system, an input content in a source language; translating, by the server system, the input content in a target language based, at least in part, on implementation of a translation model; and performing, by the server system, time-synchronization on the translated input content based, at least in part, on implementation of an integer linear programming (ILP) model.

2. The method as claimed in claim 1, wherein the input content is received in at least one format.

3. The method as claimed in claim 1, wherein the at least one format comprises text, audio, video, animation, or combinations thereof.

4. The method as claimed in claim 1, wherein the translation model comprises at least one of Generative Pre-trained Transformer (GPT) model, Dynamic Time Warping (DTW) model, Large Language Model (LLM), Recurrent Neural Network (RNN) model, Sequence-to- Sequence model, or combinations thereof.

5. The method as claimed in claim 1, wherein the time-synchronization is performed based on at least one parameter, wherein the at least one parameter comprises one of start time, end time, and ideal segment length.

6. A server system for cross-lingual translation, comprising: a processor; a memory; and a communication interface coupled to the processor and the memory, wherein the memory stores instructions, which when executed by the processor, causes the server system to: receive an input content in a source language;translate the input content in a target language based, at least in part, on implementation of a translation model; and perform time-synchronization on the translated input content based, at least in part, on implementation of an integer linear programming (ILP) model.

7. The server system as claimed in claim 6, wherein the input content is received in at least one format.

8. The server system as claimed in claim 6, wherein the at least one format comprises text, audio, video, animation, or combinations thereof.

9. The server system as claimed in claim 6, wherein the translation model comprises at least one of Generative Pre-trained Transformer (GPT) model, Dynamic Time Warping (DTW) model, Large Language Model (LLM), Recurrent Neural Network (RNN) model, Sequence- to-Sequence model, or combinations thereof.

10. The server system as claimed in claim 6, wherein the time-synchronization is performed based on at least one parameter, wherein the at least one parameter comprises one of start time, end time, and ideal segment length.

Images