282 results about "Syllable" patented technology

The invention provides a multi-language cross-culture communication assisting method and system based on a large model. The method comprises the following steps: receiving a source language audio stream during a call, calling a multi-language sound frequency harmonic modulation feature library to extract fundamental frequency harmonic intensity distribution and tone turning features, and generating a cultural acoustic fingerprint vector; based on the vector, controlling a microphone array phase difference, directionally enhancing a fundamental frequency harmonic component of a speaker and suppressing noise, and outputting a high signal-to-noise ratio spectrogram; analyzing the pronunciation rhythm and tone turning characteristics of the spectrogram, capturing the pitch jump and duration of the syllable boundary, and generating an acoustic culture label; associating the spectrogram with a target semantic library, matching harmonic distribution and a cultural context rule based on a large model, and outputting a cultural interpretation prompt containing an ambiguity resolution suggestion; and generating a calibration result according to the acoustic tag and the semantic prompt, and overlapping the dynamic floating subtitles to the face area of the speaker in the video conference picture. According to the invention, cultural tone ambiguity in multi-language communication is eliminated.

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 discloses a voice cloning system and method fusing rhythm characteristics, belongs to the technical field of voice synthesis and natural language, and is applied to the aspect of fine-grained rhythm control in zero-sample voice synthesis. The implementation method comprises the following steps of: 1, extracting rhythm features and audio features of an audio file, and further respectively acquiring pause features, speed features and tone features in the rhythm features by sequentially adopting transcriptional text inverse coding, syllable-level speed registration quantization and pitch sequence feature splicing modes; 2, fusing the features of the audio files in a manner of discarding feature screening without guidance of a classifier; 3, generating a target Mel spectrogram based on conditional flow matching; generating a target audio file from a to-be-cloned audio file through the trained voice cloning model controlled by the fusion rhythm; compared with the prior art, fine-grained rhythm control of tone and rhythm feature decoupling is realized in zero-sample speech synthesis, so that intonation accuracy based on a context scene is improved.

The invention discloses a natural statement decoding method and device based on high-density electrocorticogram. The method comprises the following steps: acquiring an electroencephalogram signal acquired based on the high-density electrocorticogram; different frequency band signals are extracted from the electroencephalogram signals; the signals of different frequency bands comprise high gamma frequency band signals and at least one frequency band signal with the frequency lower than that of the high gamma frequency band signals; acquiring a voice starting point corresponding to each frequency band signal, and determining a target voice starting point based on the voice starting point corresponding to each frequency band signal; after the target voice starting point, acquiring a syllable classification result and a tone decoding result corresponding to each frequency band signal, determining a target syllable classification result based on the syllable classification result corresponding to each frequency band signal, and determining a target tone decoding result based on the tone decoding result corresponding to each frequency band signal; and determining a target tone language corresponding to the electroencephalogram signal based on the target syllable classification result and the target tone decoding result. According to the scheme, the natural statement decoding accuracy can be improved.

The invention relates to an anti-fraud outbound recognition method for cross-number-segment voiceprint tracking, and the method comprises the steps: introducing a time domain voiceprint adversarial extraction mechanism, and adding an adversarial voice discriminator, an active inhibition device, a channel, and the interference of non-identity factors of language emotion in voiceprint modeling; constructing a speaker constant feature residual channel: taking stable syllable fragments including vowels and gutto vowels in call content as a reference extraction window, and filtering emotion or speech speed driving components; outputting a steady-state voiceprint contour flow as a unique basis for subsequent cross-number identity fusion; designing a dynamic number fusion graph based on the voiceprint steady-state flow; each time of voiceprint appearance is regarded as a single time point node, and all similar historical voiceprints form a serial number merging path; a number time sequence edge weight function is set; the occurrence time interval, the use frequency and the regional jump of the new number and the old number are synthesized, and whether the number belongs to the same user or not is evaluated; and establishing atlas connection between the voiceprint main body node and all number nodes thereof.

A speech wake-up method, an electronic device, and a storage medium are provided. The method includes: performing a word recognition on a speech to be recognized to obtain a wake-up word recognition result (S210); performing a syllable recognition on the speech to be recognized to obtain a wake-up syllable recognition result, in response to determining that the wake-up word recognition result represents that the speech to be recognized contains a predetermined wake-up word (S220); and determining that the speech to be recognized is a correct wake-up speech, in response to determining that the wake-up syllable recognition result represents that the speech to be recognized contains a predetermined syllable (S230).

The embodiment of the invention provides a data processing method. The method comprises: in response to a first operation executed by a user, obtaining a first video, the first video comprising a first image set and a first voice, the timestamp of each frame of image in the first image set being the same as the timestamp of a corresponding first syllable in the first voice; in response to a second operation executed by the user, playing a second video; the second video comprises the first image set, a stretched first voice and background music, and the timestamp of a stretched first syllable in the stretched first voice is the same as the timestamp of a beat in the background music; the timestamp of each frame of image in the first image set in the second video is the same as the timestamp of the corresponding stretched first syllable in the stretched first voice.

The embodiment of the invention provides an Alzheimer's disease early warning analysis method and system based on multiple Chinese syllables. The method is applied to the technical field of data processing, and comprises the following steps: collecting voice data of an Alzheimer's patient, performing data perception, and constructing a multi-syllable sample; performing data preprocessing by adopting a multi-level graph embedded sparse feature learning method to obtain multi-syllable voice output features; constructing a classifier, carrying out feature optimization processing on the output features, and outputting to obtain a cost sensitive matrix; a cost-sensitive weighted objective function is constructed on the basis of the cost-sensitive matrix, a multi-syllable fusion analysis diagnosis result is obtained through calculation, and the Alzheimer's disease probability is predicted according to the diagnosis result; and guiding and suggesting the patient according to a probability prediction result. In this way, simple Chinese syllables are adopted for patient data collection, the Alzheimer's disease early warning scheme is obtained in combination with a multi-syllable fusion analysis method, and the accuracy of early warning analysis is improved.

A terminal device for executing a Korean reading program, according to an embodiment of the present invention, comprises: a word search and identification unit that provides a word search and identification interface that supports a field-specific category and a translated word of a word of interest specified by a user from among English words belonging to the field-specific category and supports the translated word to be flickered and displayed, or a color of the translated word to vary within a rectangular border; a phoneme combination unit that interworks with the word search and identification unit, and provides a phoneme combination interface that supports information regarding a combination step (order) of phonemes for syllables of the translated word and pronunciation information and phonetic symbols regarding the phonemes and syllables, so as to be output; and a combination exercise unit that generates syllables by combining phoneme key information input via an interface provided by the phoneme combination unit, and combines the generated syllable information into a word.

An estimation device includes: an instructor that instructs a speaker to repetitively utter two syllables containing (i) a sound including a velar plosive in a consonant or a sound including an alveolar fricative in a consonant, and (ii) a sound including an alveolar plosive in a consonant; a voice obtainer that obtains a voice of the speaker; an estimator that analyzes a voice feature of the voice obtained, and estimates an oral function of the speaker based on the voice feature analyzed; and a presenter that presents a condition of the oral function of the speaker which has been estimated.

The invention relates to the technical field of ultrasonic inspection equipment, in particular to a handheld ultrasonic multilingual voice control method, which comprises the following steps: acquiring an input voice sequence, and extracting vowel syllables in the input voice sequence; classifying according to the extracted vowel syllables to determine the language of the input voice sequence; and acquiring a voice recognition model of the corresponding language according to the language, and recognizing the input voice sequence by using the voice recognition model to obtain an operation instruction. Aiming at the problem of poor effect of a voice control system of handheld ultrasonic equipment in the prior art, various voice recognition models of different languages are preset to realize effective recognition of control instructions of different languages; in addition, in consideration of the problem that cross-language matching cannot be performed due to low equipment efficiency, a classification process for extracting vowel syllables and predicting languages is introduced in advance, so that feature quantities involved in the classification process are reduced, the processing efficiency is improved, and the recognition method can be applied to low-power-consumption equipment.

The invention relates to the field of intelligent interaction, and particularly discloses an end-to-end voice large model interaction system, which is characterized in that a voice sensor is used for acquiring voice signals input by a user, and voice signal segmentation and embedding fusion are carried out on the voice signals input by the user by adopting a voice signal analysis and coding technology based on deep learning and end-to-end; and meanwhile, intonation mode feature extraction is performed on the user input voice signals, so that answer voice signals are intelligently generated according to fine-grained compensation fusion representation between features obtained after embedding fusion of the syllable signals of the user input voice and intonation features of the user input voice. In this way, the system can better understand the intention and emotion of the user, thereby providing more humanized and natural response. And meanwhile, the method can adapt to different language environments, so that the adaptability and generalization ability of the system are enhanced.

Systems and methods evaluate the meanings of whole words bound within the meanings of their parts by analyzing and identifying features and components of words (such as letters and syllables), using sound information, for example phoneme class structure, to find sound correlation and using synonymity to find meaning correlation between words (or word parts) and a data set of known / identifiable sound / meaning associations called morphemes to find morpheme‑word‑parts that optimally describe an input word. Various means are described of compiling morphemes into a data set. The systems and methods further include analyzing the semantic functions of word parts and their modifying interrelationships, and using identifications made by one or more analyses. The systems and methods can evaluate or improve understanding of word meanings. The systems and methods are useful in researching sound / meaning associations in language generally and for improving language processing generally by the incorporation of more semantic information.

The invention relates to the technical field of data processing, in particular to a voice wake-up method and device, computer equipment, a storage medium and a program product. The method comprises the steps that voice data input by a user are acquired, feature extraction is carried out on the voice data through a target model, voice features corresponding to the voice data are obtained, and the target model is a pre-trained ASR model; based on a mapping relation table and a pronunciation dictionary of the target model, text features corresponding to the registered text are determined, the mapping relation table is constructed based on a hidden layer feature layer of the target model, and the hidden layer feature layer comprises a corresponding relation between syllables and hidden layer features; and determining similarity data of the voice features and the text features, and determining whether to execute the wake-up task according to the similarity data. By adopting the method, the awakening accuracy can be improved.

The invention provides a model training method, a voice recognition method, a voice recognition device, electronic equipment, a computer readable storage medium and a computer program product, and relates to the technical field of voice processing, the training method comprises the following steps: obtaining a target sequence, the target sequence being an audio feature sequence of sample voice data with noise; inputting the target sequence into a self-attention layer of a speech recognition model, and controlling the self-attention layer to perform attention processing on local sub-features in the target sequence and context features of the local sub-features to obtain sub-features after attention processing; obtaining a text recognition result of the sample voice data based on each sub-feature after attention processing; and training a speech recognition model based on the difference between the text recognition result and the labeled text carried by the sample speech data. According to the invention, the accuracy and response speed of the speech recognition model for recognizing the keyword syllables in the sound signals can be improved, and the man-machine interaction experience is improved.

The invention discloses a Tibetan word learning method based on image recognition and speech synthesis, and the method comprises the following steps: carrying out the standardization processing of an image containing Tibetan words, then recognizing Tibetan characters in the image, and generating a corresponding character sequence. And constructing data containing syllables and pronunciation information through phoneme segmentation, and generating input content for controlling speech synthesis. And generating a voice signal matched with the original image content by using the improved voice synthesis model in combination with the syllable labels, the rhythm factors and the voice features. The images, the recognized characters and the voice are integrally displayed in the learning terminal, linkage feedback from the characters to the voice is achieved, and the interaction effect and efficiency of Tibetan learning are improved. According to the invention, the end-to-end feedback of the learning process is realized in a mode of combining image recognition and speech synthesis.

The invention discloses a hierarchical rhythm mapping-driven short video copywriting mood automatic adjustment method, and relates to the technical field of video processing, and the method comprises the steps: 1, receiving a text character string and a language type identifier, and building an occupation column for bearing a tone mark, an accent mark and a duration mark at each level; 2, dividing each sentence into phrase segments based on the hierarchical index table, freezing boundaries by taking the phrase segments as units, presetting sentence end termination styles according to punctuations, determining kernel phrases according to semantic anchor points, initializing trends of the kernel phrases, and performing time sequence elastic alignment and hierarchical backfilling to obtain a sentence end termination pattern; and finally outputting a triple sequence which covers all syllables and is composed of tone marks, accent marks and duration marks as a target rhythm control sequence. And step 3, performing audio generation based on the target rhythm control sequence to obtain new dubbing. According to the method, the tone accuracy and expressive force of short video dubbing are improved, and the time and cost of manual adjustment are remarkably reduced.

The present invention proposes a method and system for integrating Tibetan word segmentation and part-of-speech tagging, and relates to the field of electronic information. The method provided by the present invention obtains Tibetan text information input by a user, calls an integrated model and segments Tibetan syllables and non-Tibetan character blocks, performs CRF prediction, obtains the optimal label prediction, and sorts the writing form of each Tibetan syllable according to the result of the label prediction to obtain the corresponding tagging result. In this application, by proposing to establish a corresponding integrated model, Tibetan word segmentation and part-of-speech tagging are performed based on the corresponding integrated network. Compared with previous solutions, due to the integrity of the integrated model introduced in this application, the problem of cumulative part-of-speech tagging errors caused by word segmentation errors due to the two-stage execution of domain word segmentation and part-of-speech tagging tasks is avoided, and the corresponding Tibetan word segmentation and part-of-speech tagging can be processed more accurately, further increasing the practicality of the solution.

The invention provides a sensitive word dynamic monitoring method and device in Tibetan complex environment speech recognition, and the method comprises the steps: carrying out the segment segmentation of a to-be-monitored Tibetan speech input stream in a complex environment, and obtaining the framed speech signals of the Tibetan speech in the complex environment; performing multi-dimensional perceptual fusion on the phoneme-level score, the semantic-level score, the scene-level score and the emotion-level score of each candidate sensitive word in the framing voice signals to obtain perceptual fusion features of the context corresponding to each candidate sensitive word; determining the decoding confidence coefficient of the Tibetan syllable sequence through the path stability of a decoding path in speech recognition and the posterior probability of each Tibetan syllable in the Tibetan syllable sequence; and determining the context sensitivity of each candidate sensitive word based on the decoding confidence and each perceptual fusion feature, and carrying out graded early warning on the Tibetan speech input stream in the complex environment through the context sensitivity. Based on the scheme, multi-dimensional fusion scoring of sensitive words in Tibetan complex environment speech recognition can be realized.

The invention discloses an entity word error correction method and device based on an editing distance, electronic equipment and a medium, and the method comprises the steps: obtaining a voice recognition text, inputting the voice recognition text into a pre-trained entity word detection model, and obtaining a to-be-corrected first entity word; determining a first syllable sequence of the first entity word; obtaining a plurality of second entity words according to a preset entity word list, and determining a second syllable sequence of each second entity word; determining an editing distance between the first entity word and the second entity word according to the first syllable sequence and the second syllable sequence, and selecting a target entity word from the second entity word according to the editing distance; and replacing the first entity word in the speech recognition text with the target entity word. According to the invention, entity word error correction is carried out on the basis of the speech recognition result by using the editing distance, the speech recognition efficiency and accuracy are improved, and the method can be widely applied to the technical field of speech recognition.

The invention discloses a method for processing line end splitting syllables of a Taibe. The method comprises the following steps of: firstly, carrying out typesetting processing on contents needing to be typeset and displayed in the Taibe according to a rule from left to right and from top to bottom; judging whether the word at the end of the line needs to be subjected to syllable splitting or not, processing the word needing to be subjected to syllable splitting according to a Taibo language syllable splitting rule, and adding a zero-width connection symbol U200D at the end of the line to ensure that the Taibo alphabet at the end of the line after syllable splitting is displayed in a word form; changing the whole Yinberg word of which the syllables cannot be split to the next line; calculating the size of a line blank generated by syllable splitting or line feed of the whole word; according to the principle of uniform distribution, row spaces are distributed to corresponding word space positions; when syllable splitting processing is carried out, the zero-width connector code U200D is added to the downlink line head, it is ensured that the Taibo alphabet of the line head is displayed in a word form, and Taibo line end syllable splitting typesetting processing on all electronic devices can be achieved.

The embodiment of the invention belongs to the field of intelligent interaction, and relates to an interaction method of intelligent glasses and a translator, which comprises the following steps: the intelligent glasses synchronously acquire voice information of a user through a plurality of microphone matrixes; the voice information is transmitted to a translator in real time through a wireless communication protocol, a timestamp is added to each frame of voice information, and the translator detects transmission delay according to the timestamps and dynamically adjusts the transmission rate of the voice information; the translation machine divides the voice information into different subunits for processing according to syllables, intonations and grammar by adopting a preset acoustic model segmentation processing algorithm; the translation machine translates the voice information by using a vocabulary selection algorithm based on a language environment; and the translation machine feeds back a translation result to the intelligent glasses through an incremental feedback algorithm. The invention further provides an interaction system of the intelligent glasses and the translation machine. The objective of the invention is to realize low-delay and high-accuracy translation result feedback while ensuring high-precision speech recognition and rapid translation.

An authentication system includes an acquisition unit configured to acquire a voice signal of an utterance voice of a speaker; a detection unit configured to detect a first utterance section during which the speaker is uttering from the voice signal and a second utterance section during which the speaker is uttering from voice signals of a plurality of speakers registered in a database; a determination unit configured to collate a first voice signal of the first utterance section with a second voice signal of the second utterance section and determine an authentication condition for authentication using the first voice signal based on a length of the second voice signal of the second utterance section or the number of syllables included in the second utterance section; and an authentication unit configured to authenticate the speaker based on the authentication condition.

The invention relates to a language decoding method and device based on electroencephalogram signals and electronic equipment, and the method comprises the steps: collecting the electroencephalogram signals of a subject, and carrying out the preprocessing of the electroencephalogram signals, and obtaining a neural feature time sequence; the neural feature time sequence is input into a parallel decoding architecture, and the parallel decoding architecture comprises at least two decoding branches and is used for decoding subunits of different orthogonal dimensions of a language from the neural feature time sequence; obtaining a subunit probability sequence output by each decoding branch through a parallel decoding architecture; performing time sequence probability accumulation and fusion on the sub-unit probability sequence of each decoding branch to obtain a stabilized sub-unit sequence; performing legality verification and combination on the stabilized subunit sequence according to a phonetic system rule to generate a legal syllable sequence; and disambiguating the syllable sequence based on the semantic context, and outputting a continuous natural language text. According to the method, effective modeling and generalization decoding are carried out on massive syllable categories under the condition of limited training data.

The invention discloses an English vocabulary follow-up pronunciation correction method based on English teaching, and relates to the technical field of voice processing, and the method comprises the following steps: S1, constructing a standard vocabulary pronunciation unit; s2, carrying out phoneme difference positioning by using a standard vocabulary pronunciation unit; s3, performing syllable dynamic comparison by using the phoneme deviation distribution of the learner; s4, performing vocabulary level comparison by using the learner phoneme deviation distribution and the syllable deviation mapping; and S5, performing difference mapping output by using the vocabulary pronunciation difference set. By setting the difference comparison between the actual pronunciation of the learner and the standard pronunciation, an accurate alignment relationship can be established at the phoneme level, and the difference between the actual pronunciation and the standard pronunciation in the aspects of acoustic characteristics, pronunciation duration, phoneme boundaries and the like is quantized, so that compared with the prior art, only the overall similarity or fuzziness judgment can be given, and the accuracy of pronunciation is improved. And a difference comparison mechanism can provide a clearer error correction reference, so that the learner can clearly recognize the position and degree of the problem when receiving the result.

The invention discloses an intelligent microphone starting method based on voice recognition, and relates to the technical field of voice recognition. The method comprises the following steps: acquiring a user voice signal and surrounding environment audio data to obtain a mixed audio signal stream; identifying an abnormal waveform and determining a frequency feature vector; determining a waveform fluctuation amplitude and obtaining an enhanced waveform stable representation; recognizing syllable interval duration and analyzing interval shortening degree; analyzing the urgent speech speed characteristics to determine a time domain stretching compensation value, and adjusting the syllable interval duration to obtain a syllable sequence; identifying spectrum features of the environment background audio and determining candidate wake-up words; performing similarity matching with an emergency wake-up word template to determine an intention recognition result; and analyzing the confidence score to obtain an emergency response instruction execution signal. Through multi-level signal processing and intelligent feature extraction, high-precision voice intention recognition in a complex environment is realized, the robustness and reliability of an emergency response system are remarkably improved, and rapid help seeking of a user in a crisis scene is guaranteed.

The invention relates to a speech synthesis method and device, and the method comprises the steps: obtaining a to-be-synthesized text; performing phoneme conversion on the to-be-synthesized text to obtain a first phoneme sequence, the first phoneme sequence comprising at least one phoneme and a tone of a syllable where the at least one phoneme is located; decoupling phonemes and tones in the first phoneme sequence, and extracting phoneme features of the to-be-synthesized text based on a decoupled second phoneme sequence; wherein the second phoneme sequence comprises the at least one phoneme; performing text coding on the to-be-synthesized text in a syllable dimension, and extracting semantic features of the to-be-synthesized text; and performing fusion processing on the semantic features and the phoneme features, and generating voice based on the fused features. According to the invention, the synthesized voice is accurate in pronunciation and natural in rhythm, and it is ensured that the synthesized voice achieves expected effects in the aspects of naturalness, rhythm and semantic consistency.

The invention discloses an emotion category determination method and device, equipment, a storage medium and a product. The method comprises the following steps: acquiring audio data; extracting an MFCC feature of the audio data and a first feature of a pinyin syllable; converting the audio data into a text, and determining an intonation embedding feature of each word in the text, and a word embedding feature, a position embedding feature and a segment embedding feature of the text; fusing the MFCC feature, the first feature, the intonation embedding feature, the character embedding feature, the position embedding feature and the segment embedding feature to obtain a second feature; and inputting the second feature into an emotion recognition model, and determining a target emotion category corresponding to the second feature according to relationship information between a preset feature and a preset emotion category in the emotion recognition model. Various features such as voice features, text features, intonation features and tone features based on human auditory characteristics are fused, and the accuracy of determining the emotion category of the audio data is improved.

The present disclosure provides a method of recognizing speech offline, electronic device, and a storage medium, relating to a field of artificial intelligence such as speech recognition, natural language processing, and deep learning. The method may include: decoding speech data to be recognized into a syllable recognition result; transforming the syllable recognition result into a corresponding text as a speech recognition result of the speech data.

The invention discloses a mandarin training correction method for voice processing, and relates to the technical field of voice signal processing, and the method comprises the steps: recording the voice when a user reads a training text, and the amplitude-frequency feature sequence of each read character voice; constructing corresponding vowel feature vectors and initial feature vectors; detecting non-standard vowel syllables of the user; detecting non-standard initial syllables of the user; and prompting the user, and broadcasting the corresponding standard mandarin syllable pronunciation and spelling method to the user so as to train and correct the mandarin of the user. According to the method, the specific syllables of the user are accurately captured and carefully recognized, the pronunciation and spelling problems of the user are effectively pointed out, mandarin training and correction of the user are more efficient and accurate, and the method has great significance in improving the language ability and communication level of the public.