17 results about "Vowel" patented technology

The present invention relates to a system and method configured for analysing acoustic parameters of speech to detect, diagnose, predict and / or monitor progression of a condition, disorder, or disease, and more particularly, any of paediatric and adult neurological and central nervous system conditions including but not limited to low back pain, multiple sclerosis, stroke, seizures, Alzheimer's disease, Parkinson's disease, dementia, motor neuron disease, muscular atrophy, acquired brain injury, cancers involving neurological deficits, paediatric developmental conditions and rare genetic disorders such as spinal muscular atrophy. The system and method extracts a first formant data set from words spoken by an individual and uses these to classify the vowels in the words on a first computing device, such as a mobile smart phone equipped with a microphone into which an individual speaks. The system stores at least some of these frequencies for the vowel formants in a second formant data set as a recorded file and provides the second formant data set as input to acoustic metrics to generate score data from which an assessment is made to determine the articulation level of the vowels in the words spoken by the individual, allowing allow for detection, diagnosis, prediction and / or monitoring progression of the condition, disorder, or disease.

A data structure and associated methods for analysis of a continuous 1,197,000-letter unvocalized Hebrew string referred to as the Word of God (WOG). The data structure contains only the twenty-two classical Hebrew letters and their five final forms, with no spacing, punctuation, vowelization, or editorial symbols. Intrinsic placement of the final letters enables deterministic segmentation of the string into 305,490 lexical units and 23,206 verses without external conventions. Fixed letter-number assignments provide a numeric architecture for evaluating substrings, detecting alterations, identifying encoded mathematical correspondences, and performing pattern analysis. The system preserves full semantic range by supporting multiple morphologically valid interpretations of unvocalized Hebrew strings. Methods for segmentation, numeric evaluation, reconstruction, integrity verification, semantic analysis, and mathematical pattern detection are provided thereby providing a reproducible foundation for computational and linguistic research.

This product is a notebook paper with five lines representing vowels next to the writing space, with the vowels A, I, U, E, O marked at the beginning of each line, visualizing the finger movements for vowels when reading by hand. It can be used vertically or horizontally depending on the purpose. After writing the letters, mark the vowel positions on the five lines, connect the marks with lines, and visualize the vowels.

The invention provides a vowel recovery method and device, electronic equipment and a storage medium, and belongs to the technical field of natural language processing, the vowel recovery method comprises the steps that a first to-be-processed text is acquired, and the first to-be-processed text comprises a first text needing vowel recovery; the first to-be-processed text is input into the vowel recovery model for variable note label synchronous prediction, prediction labels output by the vowel recovery model are obtained, and the prediction labels are three types of variable note labels corresponding to each character in the first text; and determining a target text after vowel recovery based on the first to-be-processed text and a prediction label output by the vowel recovery model. In the process, the three types of variable note tags corresponding to each character in the first text can be synchronously predicted, and conflicts among different variable note tags are avoided, so that the variable note tag is accurately predicted for each character in the first to-be-processed text, and the accuracy of vowel recovery is improved.

The edge device-oriented lightweight voice band expansion method, device, terminal and medium provided by the application belong to the technical field of voice signal processing, and the method comprises the following steps: obtaining a logarithmic domain narrowband audio signal amplitude spectrum and a logarithmic domain mixed amplitude spectrum; constructing a white noise amplitude spectrum; inputting the logarithmic domain narrowband audio signal amplitude spectrum, the logarithmic domain mixed amplitude spectrum and the white noise amplitude spectrum into a trained voice bandwidth expansion model to generate a first high-frequency component corresponding to a consonant in the narrowband audio signal and a second high-frequency component corresponding to a vowel in the narrowband audio signal, and then obtaining a predicted amplitude spectrum; expanding phase information of the narrowband audio signal, generating a wideband audio signal according to the predicted amplitude spectrum and the expanded phase information of the narrowband audio signal, and outputting the wideband audio signal. The voice bandwidth expansion model is used to reconstruct the consonant, so that the reconstructed consonant component has higher fidelity, and the intelligibility of the reconstructed voice is ensured.

The application provides a vowel restoration method and device, electronic equipment and storage medium, and belongs to the technical field of natural language processing, and comprises the following steps: obtaining a first to-be-processed text, the first to-be-processed text comprising a first text requiring vowel restoration; inputting the first to-be-processed text into a vowel restoration model for multi-vowel symbol label synchronous prediction to obtain a predicted label output by the vowel restoration model, the predicted label being three types of vowel symbol labels corresponding to each character in the first text; and determining a target text after vowel restoration based on the first to-be-processed text and the predicted label output by the vowel restoration model. In this process, three types of vowel symbol labels corresponding to each character in the first text can be synchronously predicted, avoiding conflicts between different vowel symbol labels, so that the vowel symbol label of each character in the first to-be-processed text can be accurately predicted, thereby improving the accuracy of vowel restoration.

The Chinese phonetic alphabet tone corner code labeling method is used for supplementing and expanding a currently executed Chinese phonetic alphabet scheme in actual use and is used for reading correct Chinese phonetic alphabets. An existing Chinese pinyin scheme is composed of letters and tones, and a standard mode that pronunciation of Chinese characters is displayed in an integral up-and-down structure mode is adopted. However, on the basis of an information digital technology, information is expressed in a one-way structure mode from left to right, and pronunciation of Chinese characters is often directly expressed by phonetic alphabets without upper tones. According to the scheme, a single form of an up-and-down structure of which tones are marked on finals in a Chinese pinyin scheme is expanded in a left-and-right structure form, and the scheme is more suitable for a left-to-right structure expression mode of a modern information digital technology. The method can complement the missing tones of the existing Chinese pinyin scheme in use, provides correct Chinese character pronunciation, and does not affect listening, speaking, reading and writing of Chinese characters. Strengthening Chinese phonetic alphabets is an important role as a tool for assisting Chinese character pronunciation.

This disclosure provides a method, apparatus, device, and storage medium for segmenting Chinese speech signals. The method includes: sampling a target audio signal containing speech corresponding to a target Chinese text to obtain signal amplitudes corresponding to multiple sampling points; performing speech endpoint detection on the target audio data based on the signal amplitudes corresponding to the multiple sampling points to obtain multiple speech segments in the target audio data; determining the vowel position sequence corresponding to the target speech segment based on the formant energy of the speech signal in the target speech segment; determining syllable segmentation points and initial / final segmentation points of the target speech segment based on the signal amplitudes of sampling points between two adjacent vowel positions in the vowel position sequence and the initials and finals of the corresponding Chinese text segment; and segmenting the target speech segment based on the syllable segmentation points and initial / final segmentation points to obtain segmented speech primitives. This disclosure improves the accuracy of speech signal segmentation.

This invention discloses a method for recognizing vocal styles based on time-frequency refined analysis. The process is as follows: First, a fundamental frequency estimation and harmonic labeling step is performed. The short-duration vowel portion of the vocal signal is first used for fundamental frequency estimation. This estimation employs a frequency estimation algorithm combining time-domain autocorrelation and narrowband spectral energy. Then, based on the estimated fundamental frequency, adaptive harmonic labeling is performed on the spectrum to accurately identify all harmonics. Next, a time-frequency refined analysis step is performed. The vocal signal is refined and features are extracted from both the time and frequency domains, with a focus on periodic variations and harmonic structures. Finally, a support vector machine (SVM) model training and recognition step is performed. The extracted features and corresponding vocal style labels are used to train the SVM model. After training, the model can be used for vocal style recognition. The style is obtained by using the feature vectors extracted from the vocal signal as input.

The invention discloses a multi-dimensional speech adaptive training method and system. The method comprises the following steps: collecting historical training data and training time intervals of a user, and judging whether vowel training is started or not; acquiring real-time training data of a user, and sequentially performing loudness judgment, pitch exercise judgment and vowel space area standard judgment; and respectively calculating corresponding vowel space area target adjustment coefficients according to the age and gender classification of the user, and entering different training formulating modes based on the size comparison of the second target and the first target to generate a personalized vowel exercise plan. Through dynamic acquisition and quantitative analysis of multi-dimensional voice parameters, self-adaptive adjustment of training content and difficulty is realized, manual fine adjustment is supported, the problems of parameter solidification, insufficient personalization and weak feedback mechanism in an existing voice training system are effectively solved, and the pertinence, adaptability and rehabilitation effect of training are improved.

The invention discloses a classical poem recitation assisting method fusing initial and final analysis, and relates to the technical field of digital education, and the method comprises the steps: achieving the intelligent judgment and prompt of recitation rhythm and escort state based on the structural collection and analysis of recitation audio signals; the method comprises the following steps: acquiring and dividing a recitation audio into word segments through a voice recognition technology, and generating corresponding audio feature data information and text feature data information in combination with a poem structure; then judging whether the recitation duration is consistent with the poem structure or not through sentence level comparison, and providing a duration adjustment suggestion when the recitation duration is abnormal; on the basis that the recitation rhythm is normal, the vowel acoustic vector of each character is further extracted, and multi-level escort auxiliary suggestions are generated through initial and final difference analysis of the last character of adjacent sentences; according to the invention, through a process closed loop combining automatic acquisition, intelligent analysis and real-time feedback, the escort sensitivity and rhythm control ability of a learner are significantly improved.

The application relates to an improved pathological voice generation model and a construction method thereof, which comprises the following steps: S1, using all available pathological / a / vowel speech records under normal pitch in a sound data set as a training data set; S2, a training process is divided into training of a generator and training of a discriminator, both of which are alternately performed, the generator comprises a latent mapping network module and a transposed convolution and multi-receptive field fusion module; the discriminator comprises four discriminators with different cycle settings, which are used for evaluating and training intermediate waveform outputs after each upsampling of the generator; and S3, inputting a random noise vector into the trained generator, converting the random noise vector into new audio data by the generator, performing standardization processing on the generated audio data, and repeating the above steps to generate multiple new data.

A method, apparatus, and computing device for audio processing of lung function indicators are provided. The method can include obtaining at least one item of identity information related to a first user, obtaining at least one audio segment related to the first user, the at least one audio segment including at least one of a first audio segment containing a cough sound emitted by the first user, a second audio segment containing a blowing sound emitted by the first user, or a third audio segment containing a vowel sound emitted by the first user, processing the at least one audio segment to obtain a processed audio segment, determining, based on the at least one item of identity information, at least one predicted value of at least one lung function indicator of the first user, and determining, based on the at least one predicted value and the processed audio segment, a final predicted value of the at least one lung function indicator of the first user via a machine learning model.

Embodiments include systems and methods for detecting physiological parameters or health metrics using segmented audio signals. A system prompts a user to produce structured vowel sounds, minimizing audio conditioning or modulation artifacts, such as automatic gain control. The system analyzes voiced-sound segments using time-domain and / or frequency-domain techniques to extract a fundamental frequency and identify periodic modulations corresponding to cardiac pulses. Signal integrity criteria are applied to ensure robust data quality. Physiological parameters, such as heart rate, heart rate variability, and additional metrics, are generated based on the detected modulations. Real-time alerting and feedback mechanisms enable reacquisition of audio samples when signal quality is insufficient and fails quality or signal criteria thresholds or fails expected metrics thresholds.

This invention relates to the field of speech synthesis technology, and more particularly to a voice cloning method, apparatus, and storage medium based on TTS (Text-to-Speech) technology. The method includes: acquiring target audio data and preprocessing the target audio data; extracting target acoustic features from the preprocessed target audio data and constructing a target acoustic parameter database based on the extracted target acoustic features; acquiring target text and its sentiment tags, and generating a phoneme sequence and a target parameter sequence; traversing the phoneme sequence to locate vowel nodes, and optimizing the target parameter sequence based on the location results; generating synthesized audio based on the target parameter sequence, and performing a voiceprint consistency check on the synthesized audio. This invention significantly reduces the dependence on original recording data and production costs, and greatly improves the efficiency of voice generation.

This table game involves combining multiple hexahedrons that display letters or symbols to create words, and provides a toy that can be enjoyed by many people. [Solution] The toy is composed of multiple hexahedrons, each having a face that displays a single character or symbol. The characters displayed on the hexahedrons are hiragana or katakana. When the characters displayed on the hexahedrons are hiragana, the hexahedrons consist of two rectangular prisms for each of the 45 types of unvoiced characters such as "a, i, u, e, o", one rectangular prism for each of the 20 types of voiced characters such as "ga, gi, gu, ge, go", one rectangular prism each for the semi-voiced characters "pa, pi, pu, pe, po", one rectangular prism each for the small characters "ya, yu, yo, tsu", and one rectangular prism for the long vowel mark.

A method of providing an instructional scaffold to person learning to read English language comprises displaying printed matter to the learner in the form of list, preferably text stories. The normal spacing of the letters in words is kept intact; in a first part of the method the rime portions of monosyllable words are bolded and made larger than the onset portions; and in a second part of the method, which also may be used independently, the sequential syllables of multisyllable words are emphasized by alternating plain font with bolded font and the long vowels are identified, for example by underscore, again with the words of any text being kept intact.