46 results about "Speech patterns" patented technology

A system for contextual analysis and metadata database generation for user-specific speech patterns is disclosed. The system accesses a speech signal of a user and identifies the user based on the voice print associated with the user. The system splits the speech signal into a first set of audio frames, where each audio frame comprises an utterance of one or more words. The system determines a context associated with each word. In response, the system detects a context change between a first text and a second text. The system generates a contextually split set of frames by splitting the speech signal into a second set of audio frames according to the detected context changes.

Examples relate to computer-implemented methods for analyzing communication in digital evaluation. A computing device accesses multimodal data comprising video and audio information of human subjects and configures a computational model using this data to identify patterns in communication that correlate with assessment metrics. The configuring implements processing techniques that preserve relationships between features across different modalities. When a video recording of a candidate is received, the computing device processes the video using the configured computational model to extract communication features. These features may include facial expressions, gestures, eye movements, posture, vocal tone, and speech patterns. The device generates an evaluation of the candidate based on the extracted communication features and outputs a representation of the evaluation.

A system for an audio slicing window selection for contextually splitting a speech signal is disclosed. The system identifies a first audio processing software algorithm that is assigned to a user. The system identifies a set of audio processing software algorithms and configures each of them with a respective audio slicing window. The system selects a second audio processing software algorithm, from among the set of audio processing software algorithms. The system selects one of the first and second audio processing software algorithms that is assigned an audio slicing window associated with the context of the speech signal. The system splits the speech signal using the selected audio processing software algorithm. The system determines whether the speech signal is split contextually. In response to determining that the speech signal is not split contextually, the selected audio processing software algorithm and / or the audio slicing window may be updated.

Systems and methods for generating dynamic conversational recommendations. Conversational recommendations include communications between a user and a system that may maintain and / or facilitate (e.g., via autocomplete functionality) a conversational tone, cadence, and / or speech pattern of a human during an interactive exchange between the user and the system. The system may use artificial intelligence applications to generate suggested dynamic conversational recommendations based on initial user inputs (e.g., such as autocomplete functionality).

Systems and methods for generating dynamic conversational recommendations. Conversational recommendations include communications between a user and a system that may maintain and / or facilitate (e.g., via autocomplete functionality) a conversational tone, cadence, and / or speech pattern of a human during an interactive exchange between the user and the system. The system may use artificial intelligence applications to generate suggested dynamic conversational recommendations based on initial user inputs (e.g., such as autocomplete functionality).

Systems and processes are disclosed for real-time fraud detection and prevention in in-person transactions. The invention utilizes an AI / ML engine to analyze customer application data for inconsistencies and unusual requests indicative of potential fraud. Concurrently, a real-time conversation analysis engine with speech recognition algorithms monitors interactions between bank associates and customers, identifying suspicious speech patterns, hesitations, and keywords associated with scams. By combining insights from application data and conversational analysis, the system generates a comprehensive risk assessment. When a high probability of fraud is detected, an alert notifies the bank associate, security personnel, and other relevant individuals. This proactive approach enables immediate action to prevent fraudulent transactions, reducing manipulation risks and minimizing financial losses. The system continuously learns from new data, adapting to evolving fraud tactics, thus providing robust, long-term protection for financial institutions and their customers.

A system for contextual analysis and metadata database generation for user-specific speech patterns is disclosed. The system accesses a speech signal of a user and identifies the user based on the voice print associated with the user. The system splits the speech signal into a first set of audio frames, where each audio frame comprises an utterance of one or more words. The system determines a context associated with each word. In response, the system detects a context change between a first text and a second text. The system generates a contextually split set of frames by splitting the speech signal into a second set of audio frames according to the detected context changes.

The application discloses a meeting window call system based on a voice sensor and relates to the technical field of communication.The meeting window call system comprises a full-space voice collection module, a dynamic confidence evaluation module, a phase coupling enhancement module, a multi-dimensional speech mode recognition module, a dynamic gain adjustment and sound quality enhancement module and a full-link voice integrity verification module.The full-space voice collection module is arranged in a meeting window space based on audio reflection characteristics, an array type voice sensor unit is arranged, a multi-frequency full-space collection matrix is constructed, voice signals of both parties in the meeting are collected, and basic audio data streams with high signal-to-noise ratio are output.The application realizes accurate identification and faithful enhancement of low-volume voice through array type sensing collection, dynamic confidence evaluation, phase coupling and harmonic enhancement, multi-dimensional speech recognition and dynamic gain adjustment, and ensures that weak signals are not missed and distorted through full-link integrity verification and false positive backtracking, thereby improving the privacy, security and information integrity of the meeting call.

The system according to this embodiment aims to provide a personalized experience that responds to the individual needs and personalities of users. [Solution] The system according to the embodiment comprises a learning unit, an agency unit, and a clone creation unit. The learning unit learns the user's speech patterns and thought patterns. The agency unit functions as the user's agency based on the information learned by the learning unit. The clone creation unit creates a clone of the user.

Aspects relate to systems and methods for determining actor status according to behavioral phenomena. An exemplary system includes an eye sensor configured to detect an eye parameter as a function of an eye phenomenon, a speech sensor configured to detect a speech parameter as a function of a speech phenomenon, and a processor in communication with the eye sensor and the speech sensor; the processor is configured to receive the eye parameter and the speech parameter, determine an eye pattern as a function of the eye parameter, determine a speech pattern as a function of the speech parameter, and correlate one or more of the eye pattern and the speech pattern to a cognitive status.

Methods and systems are described herein for generating dynamic conversational responses. Conversational responses include communications between a user and a system that may maintain a conversational tone, cadence, or speech pattern similar to a human during an interactive exchange between the user and the system. The interactive exchange may include the system responding to one or more user actions (which may include user inactions), and / or predicting responses prior to receiving a user action.

One or more methods of using location-based trivial questions with emotional response capabilities in a vehicle include initiating one or more location-based trivial questions based on global positioning system (GPS) coordinates, and detecting active players using one or more microphones within the vehicle. Based on player names and seat locations, the method includes customizing trivial questions for one or more of the seat locations. The method may further include evaluating the player's emotion by analyzing the voice tones and the voice patterns, and based on the emotion on one or more trivial questions, and / or interrogating the player's name via the microphone (s) at the beginning of the trivial question, and / or configuring the number of channels and the processing chain topology based on the detected number of active players and their seat locations.

A method, or methods, of using location-based trivia with mood-responsive capabilities in a vehicle includes initiating one or more location-based trivia questions based on global positioning system (GPS) coordinates and detecting active players using one or more microphones within the vehicle. Based on the player names and seat locations, the method includes customizing the trivia questions for one or more of the seat locations. The method may further include assessing a mood of the players by analyzing vocal tone and speech patterns and basing one or more of the trivia questions on the mood, and / or querying player names via the microphone(s) at a start of the trivia questions, and / or configuring the number of channels and processing chain topology based on the detected number of active players and their seat locations.

Systems and processes are disclosed for a multi-layered approach to fraud prevention by leveraging a machine learning engine integrated with the Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call, allowing for the potential blocking of unwanted calls. If SIP-based identification remains inconclusive, an anomaly detection engine employing the Viterbi algorithm analyzes the caller's speech patterns during voicemail messages. The Viterbi algorithm converts spoken language into text, identifying suspicious characteristics such as unusual speech patterns, inconsistencies, and keywords associated with scams. If suspicious characteristics are detected, the system automatically blocks callback attempts and notifies the customer of potential spam or unwanted calls. This proactive approach addresses both live and recorded fraudulent calls, enhancing the security of telecommunications by preventing fraudulent interactions before they can cause harm. The system continuously learns from new data, adapting to evolving fraud tactics, providing robust, long-term protection for telecommunications users.

A method includes receiving an audio signal, wherein the audio signal includes a speech component of a user and a noise component; filtering the audio signal with a self-speech filter that filters out the speech component using an intrinsic user vector, wherein the intrinsic user vector is determined based on speech input of the user during an offline training session and represents an intrinsic compressed representation of a speech pattern of the user; and outputting a filtered audio signal in which the speech component of the user has been substantially removed from the audio signal. The intrinsic user vector is obtained through machine learning using speech input from multiple speakers.

Systems, methods and / or computer program products personalizing user interactions with virtual agents of applications and / or services, using audio / visual components customized to appeal to user preferences. Upon initial interaction with virtual agents, AI ranking algorithms are triggered to adopt the highest ranked persona for the virtual agent based on previous positive interactions with the user, learned preferences, the user's state inferred from facial expressions, body language, tone. Personas can emulate voice signatures of popular characters, actors, celebrities and sports figures, and access a corpus of dialogue of the available personas to learn unique speech patterns, slang, tone, grammar, speed, and vocabulary. The corpus that comprises various personas of real and / or fictional individuals can include data of the mannerisms and visual likeness of the various characters and people, allowing avatars depicting the virtual agents to be animated in the likeness of the selected persona in real-time during conversational workflows.

Systems and methods for conversational avatar systems are disclosed herein. The systems and methods may include receiving, via a computing system, an input comprising speech data; generating, via the computing system, a transcript of the speech data in real-time; analyzing, via the computing system, the transcript in real-time; generating, via the computing system, a response to the transcript in real-time based on the tagging; selecting, via the computing system, one or more avatar animation gestures based on tone and speech patterns of the generated response; synthesizing, via the computing system, an audible response based on the generated response; synchronizing, via the computing system, the one or more avatar animation gestures to the synthesized audible response to form a synchronized avatar animation; and rendering, via the computing system, the synchronized avatar animation.

Multi-modal shortness of breath systems and methods are described. In aspects, one or more devices may be utilized to collect data associated with the user, such as audio data (e.g., speech pattern, breath, etc.) and motion data (e.g., walking, exercising, etc.) that overlaps in time with the audio data. Further, an assessment system may analyze both the audio data and the motion data collected by the one or more devices to provide an overall health and / or fitness metric for the user.

Methods, systems, and apparatus are described herein for enhanced conferencing. A computing device monitor user participation. One or more conference features may be activated or deactivated based on speech patterns of conference participants.

Systems and methods for generating dynamic conversational recommendations. Conversational recommendations include communications between a user and a system that may maintain and / or facilitate (e.g., via autocomplete functionality) a conversational tone, cadence, and / or speech pattern of a human during an interactive exchange between the user and the system. The system may use artificial intelligence applications to generate suggested dynamic conversational recommendations based on initial user inputs (e.g., such as autocomplete functionality).

The invention relates to breathing shortness estimation based on multi-modality. Multi-modal breathing shortness systems and methods are described. In aspects, one or more devices may be utilized to collect data associated with a user, such as audio data (e.g., voice pattern, breath, etc.) and motion data (e.g., walking, exercise, etc. In addition, the evaluation system may analyze both audio data and motion data collected by the one or more devices to provide overall health and / or fitness metrics for the user.

The application provides a sentiment element extraction method and system based on syntax-aware candidate matching, which specifically comprises the following steps: performing syntax analysis on input text to obtain a syntax tree to extract part-of-speech tags and phrase structures; extracting a candidate attribute word set from the syntax tree based on a preset attribute part-of-speech pattern, retrieving attribute sample examples from an example library according to text syntax vector representations corresponding to candidate attribute word phrase structures, and using the attribute sample examples to guide a plurality of large language models to generate reliable triplets; extracting a candidate opinion word set from the syntax tree based on a preset opinion part-of-speech pattern, retrieving opinion sample examples from the example library according to text syntax vector representations corresponding to candidate opinion word phrase structures, and using the opinion sample examples to guide the plurality of large language models to generate reliable quadruplets; and fusing the reliable triplets and the reliable quadruplets to obtain a sentiment element extraction result.

A system for an audio slicing window selection for contextually splitting a speech signal is disclosed. The system identifies a first audio processing software algorithm that is assigned to a user. The system identifies a set of audio processing software algorithms and configures each of them with a respective audio slicing window. The system selects a second audio processing software algorithm, from among the set of audio processing software algorithms. The system selects one of the first and second audio processing software algorithms that is assigned an audio slicing window associated with the context of the speech signal. The system splits the speech signal using the selected audio processing software algorithm. The system determines whether the speech signal is split contextually. In response to determining that the speech signal is not split contextually, the selected audio processing software algorithm and / or the audio slicing window may be updated.

The real-time tutor generation system using Artificial Intelligence for adaptive learning includes an artificial intelligence (AI) engine to generate a virtual character for adaptive and personalized learning experiences. The method involves processors that perform operations such as accessing a virtual character from a library via a user interface integrated within an online learning platform. Communication initialization between the user and the virtual character begins by receiving real-time speech input, converted to text using a speech-to-text converter. A prompt generator generates prompts for the AI engine, based on the user input. The AI engine utilizes a pre-trained Large Language Model (LLM) to match the behavior and speech patterns of specific figures, including historical, fictional, animation, and cartoon characters. The generated audio response is converted into a video featuring the virtual character speaking, enhancing the user's learning experience by integrating video with the selected character.

The system according to this embodiment aims to realize realistic customer service role-playing in a virtual space. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the user's speech patterns or responses to customer inquiries. The generation unit generates facial expressions and eye movements of a person playing the role of a customer based on the information analyzed by the analysis unit. The control unit performs customer service role-playing in a virtual space based on the information generated by the generation unit.

We provide the system. [Solution] A recording means for recording speech information of elderly people, A conversion means for converting recorded speech information into text data, A learning method that analyzes text data and learns speech patterns, A detection means for detecting anomalies based on learned patterns, A notification means that generates a notification based on the detected anomaly, A system that includes this.

To provide a management device, management system, management program, and management method that improve the accuracy of analysis of oral responses. [Solution] A management device that outputs questions for analyzing the subject's speech patterns to an information terminal and acquires information related to the subject's voice in response to the questions as response data. The management device inputs prompt data, which includes generation instruction information, into a language model to generate an estimated answer to the question based on the materials and response data, and obtains an estimated answer corresponding to the question. Alternatively, the management device inputs prompt data, which includes generation instruction information, into a language model to rewrite a standard answer corresponding to the question based on the response data, and obtains an estimated answer corresponding to the question. The management device calculates the similarity between the response data relating to the subject's voice response to the question and the estimated answer corresponding to the question.

Systems and methods for conversational avatar systems are disclosed herein. The systems and methods may include receiving, via a computing system, an input comprising speech data; generating, via the computing system, a transcript of the speech data in real-time; analyzing, via the computing system, the transcript in real-time; generating, via the computing system, a response to the transcript in real-time based on the tagging; selecting, via the computing system, one or more avatar animation gestures based on tone and speech patterns of the generated response; synthesizing, via the computing system, an audible response based on the generated response; synchronizing, via the computing system, the one or more avatar animation gestures to the synthesized audible response to form a synchronized avatar animation; and rendering, via the computing system, the synchronized avatar animation.

Pulmonary assessment based on speech can include identifying one or more audio features and speech patterns of a user's speech. A cognitive burden associated with the user's speech can be determined. A pulmonary condition of the user can be determined based on predetermined correlations between the one or more audio features and speech patterns of the user's speech, the cognitive burden, and a respiratory airway condition.

An adaptive conversational content generation system utilizes advanced technologies to provide personalized, dynamic, and emotionally intelligent interactions through a virtual voice assistant. Central to the system is a cloud server that hosts an adaptive conversational content generation service, allowing the assistant to create real-time, context-aware content based on a user's personalized profile. Key components, such as the conversation simulator, feedback analysis unit, and emotional empathy cues unit, enable the system to engage users in empathetic, meaningful conversations. Additionally, the integration of predictive modeling and conflict resolution units ensures the assistant can proactively anticipate user needs and mediate emotional conflicts. The system supports multilingual interactions, enhancing accessibility across diverse linguistic and cultural backgrounds. The system adapts to network conditions, ensuring seamless functionality. The use of Generative Adversarial Networks further refines conversational authenticity, enabling the assistant to mimic nuanced human speech patterns for more natural, emotionally resonant interactions.