461 results about "Acoustic model" patented technology

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

A task-oriented dialog system determines an endpoint in a user utterance by receiving incremental portions of a user utterance that is provided in real time during a task-oriented communication session between a user and a virtual agent (VA). The task-oriented dialog system recognizes words in the incremental portions using an automated speech recognition (ASR) model and generates semantic information for the incremental portions of the utterance by applying a natural language processing (NLP) model to the recognized words. An acoustic-prosodic signature of the incremental portions of the utterance is generated using an acoustic-prosodic model. The task-oriented dialog system can generate a feature vector that represents the incrementally recognized words, the semantic information, the acoustic-prosodic signature, and corresponding confidence scores of the model outputs. A model is applied to the feature vector to identify a likely endpoint in the user utterance.

Automatic recognition of non-verbal messages in speech, and in particular to detection or analysis of prosodic multilayered analysis of intonation units such as prosodic unit prototypes and their multi-labeled variations, may form a hierarchical classification for the analysis of non¬ verbal information or cues in speech. A speech captured by a microphone is fed to a weakly- supervised deep learning acoustic model for speech recognition and transcription, that may be based on encoder-decoder Transformer architecture, such as Whisper by OpenAI. The model is trained to output multiple words form the text in the captured speech, to identify Intonation Units (IUs) that include one or more words, and associate non-verbal labels to each of the IUs. The labels may indicate a prototype, a discourse function (such as a conversation action), an emotion, an emphasis, or an attitude, as well as a genre of a part of, or whole of, the entire captured speech.

A virtual reality (VR)-based system for improving students' reading comprehension and writing skills. The system includes: a VR headset with an integrated eye-tracking system, high-resolution stereoscopic displays and spatial audio output, where the eye-tracking system detects reading patterns, fixation points and saccadic movements to assess reading engagement; a pair of motion tracking gloves with embedded pressure-sensitive capacitive sensors and haptic feedback actuators, allowing students to work with virtual reading material, perform handwriting exercises, and receive tactile reinforcement of text-based interactions; an adaptive learning module based on artificial intelligence (AI) that is configured to dynamically adjust reading complexity and writing exercises, responding to real-time metrics for interaction with students, with the AI ​​module using deep learning-based natural language processing (NLP) models to analyze reading fluency, written coherence, and syntactic complexity; a speech recognition and synthesis subsystem comprising a neural network-based acoustic model and a phoneme segmentation processor, wherein the subsystem assesses pronunciation accuracy, fluency, and articulation deviations by analyzing phonetic waveforms of spoken responses; a multimodal interactive interface that enables students to interact with virtual reading environments via a combination of hand tracking, voice commands and eye-controlled text selection, with the interface allowing students to highlight, comment on and orally summarize text content; A real-time dashboard for teacher monitoring, which is communicatively connected to the VR system. The dashboard offers educators detailed analyses of student comprehension patterns, writing progress, and AI-generated adaptive learning recommendations; and a cloud-integrated content management system (CMS) configured to store AI-curated reading materials, personalized writing assignments, and NLP-processed insights into student performance, with the CMS enabling adaptive curriculum adjustments based on students' learning trajectories.

The invention relates to the technical field of voice decoding, in particular to a voice command word recognition post-processing method, system and device and a storage medium, and the method comprises the steps: obtaining a phoneme probability matrix and a command word path score outputted by an acoustic model; preliminarily judging whether misrecognition, mixed recognition or out-of-set word recognition exists or not based on the phoneme probability matrix and the command word path score; if the preliminary judgment result shows that misrecognition, mixed recognition or out-of-set word recognition exists, secondary confirmation is carried out based on misrecognition, mixed recognition or out-of-set word recognition, and a final recognition result is output based on a confirmation result. According to the method, the model does not need to be retrained, three types of recognition problems can be efficiently processed by utilizing a post-processing mechanism, and the recognition sensitivity and the error recognition rate can be balanced in a scene that end-side offline resources are limited.

The embodiment of the invention discloses a speech recognition method and a related device, and at least relates to a speech recognition technology in artificial intelligence, speech data to be recognized are used as input data of a time delay neural network in an acoustic model, and an output layer of the time delay neural network comprises acoustic modeling units corresponding to a plurality of syllables respectively, so that the speech recognition efficiency is improved. And the syllable probability distribution corresponding to the voice frames included in the voice data can be obtained by taking the syllables as the recognition granularity through the time delay neural network. When syllable recognition is carried out through the output layer, auxiliary judgment can be carried out on the syllables to which the voice frames belong on the basis of pronunciation rules in combination with front and back syllable information of the voice frames, so that more accurate syllable probability distribution is output. Moreover, since the syllables are generally composed of one or more phonemes, the method has higher fault-tolerant capability, not only can more accurately determine the speech recognition result based on the probability distribution of the syllables, but also has low requirements for the quality of the speech data to be recognized, and effectively expands the application scenarios of the speech recognition technology.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a cross-language voice migration synthesis method, device, equipment and medium. And training an acoustic model by adopting a hierarchical self-adaptive fine tuning strategy, fusing the phoneme sequence for reasoning and the tone mark to generate a representation sequence, and finally synthesizing a target language speech signal. According to the method, a sharing and separation parallel cross-language modeling structure is constructed, the accuracy of phoneme and tone modeling in a low-resource language is effectively improved, the target language acoustic model has higher generalization ability and migration efficiency in combination with a multi-stage self-adaptive fine tuning and training data enhancement strategy, and the method is suitable for being applied to the field of multi-language modeling. And finally, synchronous improvement of the voice naturalness and the tone fidelity is realized.

The invention discloses an ASR identification enhancement method based on audio and video recording. According to the method, the accuracy and compliance of voice recognition in the financial service interaction process are improved by fusing the audio and environment feature information in the banking business double-recording scene. The method comprises the following steps: firstly, constructing an acoustic model for a bank outlet environment, and extracting audio features and interaction scene information of conversation between a client and a worker; and then, designing a vocabulary recognition module special for the financial field, dynamically adjusting language model parameters according to professional term libraries and utterance modes of different business types, and effectively coping with key links such as financial product introduction, risk prompt and customer confirmation. Compared with a traditional ASR system, the voice recognition accuracy in the banking business handling process is remarkably improved, particularly, key term recognition and important information extraction are prominent, and more reliable technical support is provided for financial service standardized management and double-recording quality inspection.

The invention provides a self-adaptive collaborative acoustic environment active treatment system and method. The system comprises a multi-dimensional environment sensing network module, an intelligent voiceprint recognition and sound field prediction module, an active noise control module, a central intelligent collaborative regulation, diagnosis and self-learning module and a passive acoustic intervention module. The multi-dimensional environment sensing network module collects multi-dimensional sensing data in real time; the intelligent voiceprint recognition and sound field prediction module outputs a voiceprint recognition result and a noise source space coordinate and predicts a sound field evolution trend; the active noise control module outputs a residual noise signal; the central intelligent cooperative regulation, diagnosis and self-learning module outputs an active and passive cooperative control instruction set; the passive acoustic intervention module controls broadband noise to block or change its propagation path. According to the invention, through a dual-channel self-learning mechanism of the deviation diagnosis capability, continuous evolution of system performance can be realized according to a deviation autonomous optimization acoustic model and a control strategy, and accurate, efficient and prospective active treatment is carried out on a complex noise environment.

According to the sound box directional broadcasting method and device, the equipment and the storage medium provided by the invention, the initial sound box parameters and the sound field data of the target area are acquired, and the sound field data comprise the sound box array position information and the site characteristic information; constructing an acoustic model of the target area according to the site characteristic information and the initial sound box parameters, and simulating a coverage effect of sound in the target area; according to a simulation result, optimizing the initial sound box parameter to obtain a target sound box parameter; and according to the target loudspeaker box parameter, setting a loudspeaker box array position and a beam forming parameter of the loudspeaker box in the target area. According to the invention, by accurately controlling the position of the loudspeaker box array and the beam forming parameters, the sound can be ensured to uniformly cover the target area, and the common problems of non-uniform sound, interference and the like in the traditional broadcasting system can be avoided at the same time. According to the directional broadcasting method, sound can be effectively concentrated to a target area, and interference to a non-target area is reduced.

The invention provides a speech synthesis method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a phoneme sequence and a discrete acoustic feature sequence corresponding to a target text; performing speech synthesis according to the phoneme sequence and the discrete acoustic feature sequence through a pre-trained acoustic model to obtain a speech signal corresponding to the target text; wherein the acoustic model is obtained by performing multi-stage prediction task training according to a phoneme sequence and a discrete acoustic feature sequence corresponding to the training text, and a multi-stage prediction task comprises a discrete acoustic feature sequence prediction task and a Mel spectrum feature prediction task. The quality of synthesized speech can be improved.

The invention relates to the technical field of speech recognition, and discloses a real-time speech recognition method based on a Bluetooth audio stream, which comprises the following steps: analyzing bit allocation parameters to calculate quantized bit distribution and generate a frequency domain confidence mask, monitoring a packet loss concealment state flag bit of a decoder, forcibly setting the mask as a blocking threshold when an algorithm is activated, and generating a real-time speech recognition result. According to the method, a cross-level feature purification mechanism based on protocol priori and link states is constructed, acoustic model illusion caused by forged waveforms is blocked through targeted arbitration while deterministic quantization noise is eliminated, and the acoustic model recognition accuracy is improved. And the identification accuracy under a severe channel is ensured.

The invention relates to the technical field of voice recognition, in particular to a Bluetooth headset AI voice control method and system, and the method comprises the following steps: collecting a voice sample, carrying out the feature extraction of a voice signal through employing a Mel-frequency cepstrum coefficient, and generating voice feature data; and inputting the voice feature data into an acoustic model, and improving the recognition rate of the key instruction words by the acoustic model through learning features to obtain an optimized acoustic model. According to the method, acoustic feature capture is realized through voice signal feature extraction, the recognition accuracy of the key instruction is improved in cooperation with deep learning of acoustic features, the influence of different user speech speeds on the recognition effect is overcome by applying the time alignment technology of voice input, and the stability of the control instruction in different use scenes and speech speed changes is ensured. In addition, the recognition capability of a specific command is further mined and optimized by means of statistical characteristic analysis of voice, and efficient and automatic recognition and extraction of key control commands in continuous speech streams are completed.

The invention relates to the technical field of simultaneous interpretation, and discloses a simultaneous interpretation data processing method and system based on a POE microphone array. The method comprises the following steps: synchronously acquiring multi-language original audio streams and meeting place environment noise spectrum features through a distributed microphone array powered by the Ethernet; after time domain framing is carried out on the audio stream, adaptive filtering is carried out by using a dynamic noise reduction weight coefficient to obtain a primary pure voice segment; dividing the multi-language speech endpoint detection model into independent speech units with language labels through a pre-trained multi-language speech endpoint detection model, and matching a corresponding acoustic model to generate a phoneme-level time alignment sequence; comparing and outputting a term replacement instruction stream in real time in combination with a simultaneous transfer term library, and generating an intermediate semantic representation vector after fusion; and the low-delay encoder converts the voice parameter sequence into a target language voice parameter sequence, and drives the waveform synthesizer to generate final simultaneous transmission audio. The method optimizes the whole process processing, gives consideration to the simultaneous transmission accuracy and real-time performance, and is suitable for a multilingual meeting place scene.

The invention belongs to the technical field of voice generation, and relates to a voice generation method and device, equipment and a storage medium thereof. Obtaining a visual feature vector sequence of the target video frame sequence; inputting the text feature vector sequence and the visual feature vector sequence into a cross-modal fusion layer to obtain a feature fusion representation output by the cross-modal fusion layer; obtaining acoustic features output by the variant acoustic model according to the feature fusion representation; and inputting the acoustic features into a preset vocoder, and generating a target voice waveform through multi-scale convolution and up-sampling processing. According to the method, not only text features but also video features are introduced during voice generation, and voice generation is realized through cross-modal fusion features. The method is applied to financial or medical service intelligent customer service answering or service product marketing introduction scenes, and more natural and real voice is generated in combination with visual context information.

The invention provides a voice recognition method and device, electronic equipment and a storage medium, and relates to the technical field of voice recognition, and the method comprises the steps: obtaining at least one sample voice and a standard voice text corresponding to the sample voice; performing voice feature extraction on the sample voice to obtain a sample voice feature; inputting the sample voice features into the trained acoustic model to obtain acoustic modeling features output by the trained acoustic model; the acoustic modeling features and the standard voice text are input into a large language model, prediction probability distribution and prediction voice text output by the large language model are obtained, and the prediction probability distribution is used for indicating probability distribution of prediction words corresponding to the sample voice; and training a large language model based on the prediction probability distribution, the prediction voice text and the standard voice text, so that the trained large language model and the trained acoustic model have a voice recognition function. The method can improve the speech recognition accuracy.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice generation method, device and equipment based on pseudo-autoregression modeling and a medium, and the method comprises the steps: obtaining a training sample containing a text sequence, a prompt voice segment and a target semantic token sequence; performing continuous fragment mask training on the text-to-semantic model to obtain a pseudo-autoregression trained text-to-semantic model; generating candidate speech output by using the text-to-semantic model and the initial semantic-to-acoustic model which are subjected to pseudo-autoregression training, and constructing a preference data pair; updating the semantics-to-acoustics model based on the preference data pair to obtain a preference optimized semantics-to-acoustics model; and generating target voice output based on the target text and the target prompt voice. According to the method, the time sequence modeling capability of the model is enhanced through pseudo-autoregression training, and the voice generation quality is directly optimized through the preference data pair, so that the voice alignment precision and the subjective listening feeling performance are improved.

The invention relates to a voice control ultrasonic wave adjustment setting method based on a CTC-Attention mixed architecture, and the method comprises the following steps: S1, receiving a voice instruction of a doctor through a voice receiving device, and carrying out the preprocessing of an original voice signal; s2, constructing an acoustic model of a CTC-Attention hybrid architecture, and training the acoustic model to obtain a trained acoustic model; s3, inputting the preprocessed voice signal into the trained acoustic model, and performing voice recognition through CTC path probability alignment and Attention context dependence joint optimization; s4, outputting an identification result and decoding the identification result into a text instruction; and S5, analyzing the operation intention according to the text instruction, and controlling the ultrasonic instrument to execute a corresponding state adjustment operation. According to the method, the voice signal of the operator is received, the voice signal is recognized and analyzed, the obtained result is optimized and then converted into the character content with the meaning, then the ultrasonic energy with the intensity corresponding to the character content is generated by the ultrasonic instrument based on the character content, and the ultrasonic instrument is effectively used for medical operation.

The invention provides a voice enhancement and high-precision recognition method and system in a complex environment, and relates to the technical field of voice processing, and the method comprises the steps: collecting a time domain signal in an off-road parking sentry box environment for preprocessing, detecting a mute segment signal in a standard time domain signal for noise power spectral density estimation, and obtaining a noise power spectral density value; a reverberation parameter is obtained by combining voice onset information and noise spatial correlation estimation, prediction is performed by using a deep neural network model, voice masking is applied to microphone array signals to perform enhancement processing, adaptive feature extraction is performed on time domain enhanced voice signals, and a voice signal is obtained. And performing high-precision recognition on the voice adaptive feature sequence based on an acoustic model and a language model, and outputting a target recognition text. The technical problems of poor voice signal quality and low recognition accuracy in a complex noise environment in the prior art are solved. The technical effects of improving the voice signal quality and the recognition accuracy and realizing clear, accurate and real-time voice interaction are achieved.

The invention relates to the technical field of audio processing, and discloses a configuration method, system and device of a multi-room audio system and a storage medium, which are used for improving the accuracy of parameter adjustment of each audio device in the audio system. The configuration method of the multi-room audio system comprises the following steps: acquiring spatial layout information of each room and physical position information and performance parameters of each audio device; based on the spatial layout information of each room and the physical position information and performance parameters of each audio device, constructing an acoustic model comprising the plurality of rooms and the audio devices; analyzing an acoustic coupling relationship between different rooms and signal transmission characteristics of each audio device based on an acoustic model to obtain an acoustic characteristic parameter set; according to the acoustic characteristic parameter set, sound channel distribution and volume adjustment of each audio device are optimized, and an optimized audio configuration parameter set is obtained; and according to the optimized audio configuration parameter set, automatically configuring sound channel distribution, volume adjustment and audio signal transmission paths of the multi-room audio system.

The invention provides a Suzhou dialect medical voice electronic medical record conversion system and method, and the system comprises an acoustic feature extraction module which is used for extracting the acoustic features of an input voice signal; the dialect tone recognition module is used for recognizing a multi-tone system of Suzhou dialects; the voiced sound processing module is used for detecting voiced sound initial consonants in Suzhou dialects and performing acoustic feature mapping; the medical term mapping module comprises a corresponding relation library of dialect medical vocabularies and standard medical terms and a context-based ambiguity resolution unit; the speech recognition engine comprises an acoustic model, a pronunciation dictionary and a language model; and the medical record generation module is used for converting the identification result into a structured electronic medical record. According to the method, a sliding window processing strategy is adopted, continuous voice input of a doctor can be effectively processed, a long-time voice input scene is supported, and various requirements in actual clinical application are met.

The embodiment of the invention provides an acoustic model generation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. Comprising text content, audio pediatric data corresponding to the text content, text rhythm features corresponding to the text content and acoustic features corresponding to the audio pediatric data, and the acoustic model comprises a secondary rhythm representation correction module and an acoustic processing unit; inputting the text content, the audio data, the acoustic features and the text rhythm features into a second-level rhythm representation correction module for correction, and performing parameter tuning on the second-level rhythm representation correction module based on a correction result to obtain a second-level rhythm representation correction module after tuning; performing model training according to the text content, the audio data, the acoustic features and the optimized secondary rhythm representation correction module to obtain a loss function; and performing parameter optimization on the acoustic processing unit according to the loss function to obtain an acoustic model meeting a preset condition.

The invention relates to a VR glasses system for rehabilitation training, and belongs to the technical field of medical rehabilitation. Comprising a VR intelligent glasses module, a dynamic grading virtual scene is generated through a double-OLED display screen, and self-adaptive matching of scene complexity and patient cognitive level is achieved in combination with an eye movement tracking sensor; the multi-language voice interaction module integrates a dialect acoustic model and supports Cantonese, Mongolian and other offline instruction recognition. The three-dimensional motion capture module deploys a 9-axis IMU sensor network, and predicts a fall risk level in real time by using an LSTM time sequence model; the multi-terminal projection feedback module generates a three-dimensional motion thermodynamic diagram and synchronizes the three-dimensional motion thermodynamic diagram to a rehabilitation teacher and family member terminal, and the tactile feedback ground mat module comprises a multi-region pressure sensing array and a pneumatic rigidity unit, simulates mechanical characteristics of an ice surface / sand beach / cobblestone road surface and synchronizes VR scene temperature feedback. Through multi-modal data fusion and dynamic difficulty regulation and control, the training safety and rehabilitation efficiency are remarkably improved, and the method is particularly suitable for personalized rehabilitation training of dialect region patients.

The invention discloses an intelligent interaction system and method based on a far-field voice module, and relates to the technical field of voice recognizing.The system forms different processing feedback mechanisms according to different network states by capturing voice input signals of a user; recognizing and optimizing the preprocessed voice input signal in combination with an acoustic model and a language model to form a final processed text; starting a self-protection mechanism, and forming first response instruction data of a next period based on big data training; speculating second response instruction data forming a next period by using a period mode set by the system; and setting a storage space, and storing coincident response instruction data in the first response instruction data and the second response instruction data, thereby forming voice response prediction in an offline state, not only being capable of ensuring that purposeful voice interaction is realized in an offline environment and voice control is realized in a private environment, but also being capable of reducing storage for configuring offline voice, and improving the voice response prediction efficiency. And the portability of voice interaction is improved.

The invention relates to the technical field of acoustic analysis, in particular to a self-adaptive acoustic analysis method and system, and the method comprises the following steps: obtaining a sound pressure amplitude screening channel, extracting a main sound point time index, judging a fluctuation direction to generate a path identifier, and completing channel alignment and path binding to generate an analysis result. According to the invention, by acquiring the maximum sound pressure amplitude of each channel and dynamically screening the effective channel, the capability of capturing significant sound in a complex sound source environment is improved, and by combining directional extraction and fluctuation trend judgment of main sound point time index differences in a continuous period, dynamic perception and track identification of sound source feature changes are realized. The stability of path identification is enhanced by using direction consistency and time sequence alignment operation, accurate binding and real-time adaptive sound source tracking of multiple channels are completed, the identification accuracy and path continuity in a sound source dynamic change scene are effectively improved, meanwhile, redundant channel interference is avoided, and the accuracy of path identification is improved. And the processing efficiency and the adaptive capacity of the acoustic model are enhanced.

The invention discloses a speech recognition error correction method and device, a server, a computer readable storage medium and a computer program product. According to the method, a large language model is applied to a speech recognition error correction task, frame-level phoneme probability distribution generated by an acoustic model is directly and completely input into the pre-trained large language model, useful information is reserved as much as possible, and the large language model performs speech recognition correction according to global information of front and back frames. Compared with the prior art, the voice recognition correction is carried out according to the global information, the context understanding is more comprehensive, and more complex grammar errors, logic errors and fact errors can be detected in the error correction task, so that a more comprehensive, consistent and coherent result is provided.

The present invention discloses a method, device, storage medium, and electronic device for generating virtual speech. The method comprises: obtaining multiple different speech text samples and speech attribute information, wherein each speech text sample in multiple different language speech text samples corresponds to a language and an object; inputting each speech text sample into a multi-stream encoder to obtain text features corresponding to each speech text sample; and training a preset speech acoustic model based on generative adversarial network modeling using the text features and speech features to obtain a target acoustic model for generating virtual speech. The present invention can support cross-language data training and the generation of cross-language speakers. The multi-stream encoder can better capture text features in different languages, improve the flexibility and reliability of virtual preset generation, and thus solve the technical problem of low flexibility and reliability in generating virtual speech in the prior art.

The invention belongs to the field of artificial intelligence and voice recognition, and particularly relates to a voice recognition system based on an improved Transform architecture, which comprises a self-positioning module used for receiving an original audio signal, outputting a self-supervised voice feature vector and a traditional audio feature vector in parallel, and sending the self-supervised voice feature vector and the traditional audio feature vector to a feature normalization conversion module; the feature normalization conversion module is used for mapping the self-supervised voice feature vector and the traditional audio feature vector to a standard speaker feature space and outputting a normalized feature; the perception modeling module performs multi-scale time sequence coding through an improved Transform structure, and outputs a voice semantic probability distribution sequence; the CTC loss module is used for optimizing the acoustic model according to the voice semantic probability distribution sequence; the collaboration unit is used for receiving multiple paths of original audio features, screening credible channels from the obtained synchronization features, and outputting corrected features; and the fusion filtering module is used for receiving the local features and the corrected features, generating global probability distribution through attention weight fusion, and decoding the global probability distribution into a final text sequence.

The invention discloses a speech synthesis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring multi-mode background sound condition input data; performing modal integrity detection on the multi-modal background sound condition input data to obtain a detection result; generating an environment background sound feature embedding vector according to a detection result; obtaining to-be-synthesized text data and speaker reference audio data, and performing feature extraction to obtain text semantic features and speaker timbre features; inputting the environment background sound feature embedded vector, the text semantic feature and the speaker timbre feature into an acoustic model to generate a Mel spectrum; and converting the Mel spectrum into a target voice waveform to obtain synthetic voice data. By implementing the method, scene requirements can be deeply matched, diversified scene types can be covered, accurate matching of background sounds and voice semantics is realized, and the technical scheme can be applied to the fields of finance and medical health.