74 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.

The invention discloses a meeting window call system based on a voice sensor, and relates to the technical field of communication. Comprising a full-space voice acquisition module, a dynamic confidence evaluation module, a phase coupling enhancement module, a multi-dimensional speech mode recognition module, a dynamic gain adjustment and tone quality enhancement module and a full-link voice integrity verification module, an array type voice sensing unit is deployed based on audio reflection characteristics, a multi-band full-space acquisition matrix is constructed, voice signals of two parties meeting are acquired, and a basic audio data stream with a high signal-to-noise ratio is output. According to the invention, through array sensing acquisition, dynamic confidence evaluation, phase coupling and harmonic enhancement, multi-dimensional speech recognition and dynamic gain adjustment, accurate recognition and fidelity enhancement of low-volume speech are realized, and through combination of full-link integrity verification and mistaken killing backtracking, weak signals are ensured not to be missed and distorted, so that the accuracy of speech recognition is improved. And the privacy, the security and the information integrity of the meeting call are improved.

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).

The invention relates to the technical field of biological recognition, in particular to an intelligent wearable device identity authentication method and system based on biological characteristics, and the method comprises the following steps: collecting a user voice sample, removing background noise from the voice sample through a spectral subtraction method and a Wiener filter technology, and generating a denoised voice sample; separating the time sequence and frequency spectrum information of the voice signal from the de-noised voice sample by using a convolutional neural network, and extracting a time frequency spectrum feature vector; according to the method, background noise is eliminated from a voice sample by using spectral subtraction and a Wiener filter, and time spectrum features are extracted in combination with the convolutional neural network, so that the user voice capturing and analyzing capability is improved. In addition, the gating circulation unit deeply learns the intonation and rhythm of the voice data, so that the recognition of the voice mode is more accurate. The sensor data is subjected to independence analysis through the isolated forest algorithm, abnormal data can be recognized and corrected, and the accuracy of data output is ensured.

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 and method for automating sales and support interactions using computerized agents that are customized based on attributes of a target customer / buyer is disclosed. An artificial intelligence salesperson (AIS) utility / module collects / retrieves activities and attributes information about a target customer to generate a custom AIS that can be used to communicate on a sales call or a technical support call with the customer. The AIS is designed to substantially emulate a real person to better communicate with the target customer. The AIS can change on the fly for each caller, learn from sighs audible expressions of the target customer, and react to emotional changes of target customers. To create the AIS, the AI module modifies an existing speech pattern, and / or creates its own speech patterns in real time, before passing the new and / or modified scripts to the voice engine, which then “speaks” on a call to the target customer.

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.

Systems, devices, methods, and computer readable media are provided in various embodiments having regard to authentication using secure tokens, in accordance with various embodiments. An individual's personal information is encapsulated into transformed digitally signed tokens, which can then be stored in a secure data storage (e.g., a “personal information bank”). The digitally signed tokens can include blended characteristics of the individual (e.g., 2D / 3D facial representation, speech patterns) that are combined with digital signatures obtained from cryptographic keys (e.g., private keys) associated with corroborating trusted entities (e.g., a government, a bank) or organizations of which the individual purports to be a member of (e.g., a dog-walking service).

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.

A computer-implemented system for providing text to speech (TTS) comprises a user communications device 104 having an input device 134 to receive text data from a text data source; and an audio playback device 134 for outputting an audio response. The user communications device 104 being configured to receive a text data stream from a text data source. In real time, as soon as said text data stream has started to be received, a speech emphasis handler module is used to segment the incoming text data into text data segments based on natural speech patterns and / or emphasis. The text data segments are passed, as they are created and in order, to a speech recognition module to transform each said text data segment into representative digital audio data. In real time, as the representative digital audio data is generated, digital audio data is fed to an audio buffer and stored. In real time and in order, the audio segments are delivered to the audio playback device 134. The text data source may be a Large Language Model (LLM) or Large Multimodal Model (LMM) API. The application may produce natural-sounding speech in real-time, without waiting for the entire text to be downloaded.

A multimodal emotion recognition-based AI communication support system and method tailored for the elderly are disclosed. Voice data of an elderly person of a certain age or older is collected, the speech patterns of the elderly person are learned using the voice data, and communication with the elderly person is supported based on the speech patterns.

The present invention discloses a method for evaluating the naturalness of speech against speech adversarial attacks, and belongs to the field of speech recognition model security technology in intelligent speech systems. Common features of adversarial audio are obtained and common features related to unnaturalness are screened, including discontinuous time domain signals, discontinuous frequency domain signals, irregular time domain signals, irregular frequency domain signals, and abnormal speech patterns; for each common feature, the acoustic-statistical features related to unnaturalness of benign speech samples and test speech are calculated; a speech naturalness index is designed according to the acoustic-statistical features related to unnaturalness of benign speech samples, and the reference value of the speech naturalness index is the probability density distribution of the acoustic-statistical features related to unnaturalness of benign speech samples; according to the reference value of the speech naturalness index, the naturalness score of the test speech is calculated. The present invention can quantify the similarity between the test speech and normal human speech by evaluating the naturalness of the speech sample.

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.

A computer-implemented system for facilitating a text to speech application, comprising a user communications device 104 configured to communicate with a Large Language Model (LLM) or Large Multimodal Model (LMM) API, the user communications device (104) being configured to: in real time, as soon as a text data stream has started to be received, use a speech emphasis handler module to segment the incoming text data into text data segments based on natural speech patterns and / or emphasis, and pass the text data segments, in order, as they are created, to a speech recognition module to transform each said text data segment into representative digital audio data; in real time, as said representative digital audio data is generated, start feeding said digital audio data to an audio buffer and store it therein; and deliver, in real time and in order, said audio segments to an audio playback device.

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 user’s speech patterns are analysed in order to determine mental characteristics (eg. a psychological or psychiatric disorder or mental health condition such as depression, schizophrenia, bipolar disorder or psychosis risk) via different measures of coherence relating to the semantic arrangement of words, different types of connectivity graphs (eg. semantic, contextual or grammatical), and syntactic assessment of words contained within the speech sample (eg. “grammatically sophisticated”). A node 301 represents a word 303, and an edge 302 represents use of two together. Acoustic features (eg. spectral, temporal, phonation or volume parameters) may also be analysed, and the output used to select a treatment.

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.

To provide a system.SOLUTION: A communication AI system for reproducing dialogue of a specific communication robot, includes: means for storing a specific speech pattern of the communication robot; means for generating a response to an input from a user using the specific speech pattern; and means for outputting the generated response.SELECTED DRAWING: Figure 1

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.

One example method includes receiving, during a virtual conference hosted by a virtual conference provider, a first audio stream comprising speech having first speech patterns according to a first accent, the first audio stream received from a first client device associated with a first participant in the virtual conference; generating, by a first trained machine learning (“ML”) model, a second audio stream comprising the speech having second speech patterns according to a second accent; and outputting the second audio stream.