13results about How to "Improve speech recognition performance" patented technology

The invention discloses an agricultural disease and pest dialect speech intelligent recognition method based on a deep learning technology, and the method comprises the steps: combining a Mel-frequency cepstral coefficient with a convolutional neural network, and dividing a whole process into two stages, the pre-training stage comprises the steps of recording and storing a long voice file, extracting the characteristics of voice signals in the file and training a neural network model, and the retraining stage comprises the steps of recording and storing a short voice file, extracting the characteristics of the voice signals in the file, verifying a test result, adjusting model parameters and storing an optimal model; the dialect voice information reported by monitoring personnel in an agricultural field is intelligently recognized by considering accent of people in different regions, so that the recognition accuracy of complex dialect voice signals is effectively improved, and the recognition method is particularly suitable for understanding related terms of agricultural diseases and insect pests.

The invention discloses a voice recognition method and a related device, and relates to the technical field of voice recognition, and the voice recognition method comprises the steps: obtaining target voice data; the method comprises the following steps: performing preliminary voice recognition on target voice data to obtain word-level acoustic information of the target voice data and candidate voice recognition results; projecting the word-level acoustic information of the target voice data into a representation space of the large language model to obtain target acoustic features of the target voice data; and processing the target acoustic features of the target voice data into a character sequence by using a large language model and assisted by the candidate voice recognition result of the target voice data, and obtaining a voice recognition result of the target voice data. The speech recognition method disclosed by the invention has a relatively good recognition effect.

The invention discloses a domain adaptive speech recognition method and a related device, and relates to the technical field of speech processing, and the method comprises the steps: obtaining a domain adaptive speech recognition model through the training of target domain unsupervised data in advance, and obtaining a target domain dictionary when the to-be-recognized target domain speech needs to be recognized, the target domain voice and the target domain dictionary are input into the domain self-adaptive voice recognition model, the domain self-adaptive voice recognition model recognizes the target domain voice based on the target domain dictionary, and a recognition result is obtained. According to the scheme, the target domain unsupervised data is utilized in the training stage, the target domain dictionary is utilized in the prediction stage, and the recognition efficiency is improved. Therefore, the speech recognition effect of the target field can be improved.

The invention relates to the technical field of bioinformatics, and discloses an intelligent magic mirror interaction method and system based on voice recognition, and the method comprises the steps: collecting a voice signal of an intelligent magic mirror through a target multi-microphone module, carrying out the noise suppression of the voice signal, and obtaining a noise-suppressed voice signal, extracting a voice signal voiceprint of the noise suppression voice signal, determining a target voice signal and a target voice user of the intelligent magic mirror, calculating a user position of the target voice user, recognizing a user intention of the target voice signal by using a multi-language intention recognition model, calculating an intention priority of the user intention, and obtaining an intention priority of the user intention; determining a sequence intention task of the target voice user; constructing a mirror surface interaction instruction of the intelligent magic mirror, and defining a voice interaction instruction of the intelligent magic mirror; and determining an interaction behavior in the process of executing the sequence intention task, and constructing an interaction page of the target voice user and an interaction mirror surface in the intelligent magic mirror. The interaction effect of the intelligent magic mirror can be improved.

The present invention relates to a speech recognition data enhancement method based on feature clipping and translation, and belongs to the field of speech recognition processing technology. The method clips and translates the audio signal features in the time dimension and the frequency dimension respectively. Among them, in the time dimension, the feature value of a certain time period is randomly selected, the feature of the time period is clipped, and then the unclipped feature value is translated along the time dimension direction; in the frequency dimension direction, the feature value of a certain frequency segment is randomly selected, and then the feature of the frequency segment is clipped, and the unclipped feature value is translated along the frequency direction. The method does not need to regenerate the audio signal or extract features, which avoids the waste of storage space and running time. It has better data enhancement effect than the existing data enhancement technology and effectively improves the speech recognition effect.

The invention discloses a speech recognition method and device based on a multi-view lip movement image. The method comprises the following steps: acquiring to-be-processed image information and angle information; the to-be-processed image information comprises M pieces of lip movement image information; the angle information comprises N lip angles; performing fusion processing on the to-be-processed image information and the angle information to obtain target image result information; the target image result information comprises L pieces of lip movement image result information; l is a positive integer greater than or equal to 1 and less than or equal to N; and based on the target image result information, determining voice recognition result information. Therefore, the voice information can be captured more comprehensively, so that the voice recognition performance in a real scene is improved.

The application discloses a voice recognition method of an air conditioner, the air conditioner and a computer readable storage medium, and comprises the following steps: a terminal server receives voice information uploaded by a master machine; the type of the voice information is recognized; when it is recognized that the type of the voice information is a wake-up voice, adjustment information is sent to a slave machine, so that the slave machine adjusts its operation parameters according to the adjustment information, or reduces the working noise of the slave machine after adjusting its working mode. The embodiment of the application improves the voice recognition effect of the slave machine of the air conditioner in a noisy environment and improves the use effect of the air conditioner.

The invention provides a speech recognition method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a first prediction result and a second prediction result of a to-be-recognized speech, the first prediction result comprises a prediction result obtained based on a candidate text recognition result of the to-be-recognized speech, and the second prediction result comprises a prediction result obtained based on a candidate text recognition result of the to-be-recognized speech; the second prediction result comprises a prediction result obtained based on the to-be-recognized voice; determining a first weight corresponding to the first prediction result, and determining a second weight corresponding to the second prediction result; and performing feature fusion on the first prediction result and the second prediction result based on the first weight and the second weight, and determining a recognition result of the to-be-recognized voice based on the obtained fusion feature. The voice recognition effect can be improved.

The invention relates to the technical field of speech recognition, in particular to a multilingual real-time speech recognition system and method based on deep learning. The method comprises the following steps: inputting a voice signal to be recognized into a Wiener filtering model, and outputting an enhanced voice signal; processing the discrete energy spectrum by using a group of triangular band-pass filters which are uniformly distributed on frequency to obtain corresponding output, performing logarithmic operation on the output of all the filters to obtain a logarithmic power spectrum, and obtaining characteristic parameters of the voice signal based on discrete cosine transform of the logarithmic power spectrum; extracting voice signal feature vectors based on the feature parameters; and inputting the voice signal characteristic vector into a vector quantizer, outputting a code vector label formed by quantization, inputting the code vector label into the HMM voice recognition model, and outputting a recognition result. According to the invention, the accuracy of speech recognition can be improved, and the stability of real-time speech recognition service is improved.

The application discloses a noise-robust acoustic feature extraction method based on a gamma pass scaling basis vector. The noise-robust acoustic feature extraction method based on the gamma pass scaling basis vector comprises the following steps: performing pre-emphasis processing on a speech signal; performing frame processing on the speech signal after the pre-emphasis processing; performing Fourier transform on the speech signal after the frame processing; calculating a scaling coefficient according to the spectral distribution characteristics of a gamma pass filter bank, and optimizing a basis vector based on the scaling coefficient; and performing discrete cosine transform on the speech signal after the Fourier transform and the optimized basis vector to extract noise-robust acoustic features from the speech signal. According to the application, the scaling coefficient is calculated based on the frequency domain distribution characteristics of the gamma pass filter bank, and is directly applied to the basis vector for generating acoustic features, so that the original details of the speech signal are retained to the greatest extent, the information carrying capacity of the acoustic features under the interference of noise signals is ensured, and the speech recognition effect can be effectively improved.

The invention relates to the technical field of intelligent decision, and discloses an intelligent risk control voice monitoring method and system based on a multi-modal algorithm, and the method comprises the steps: carrying out the feature extraction of training voice, constructing a pre-trained text recognition model of voice features, and carrying out the model training of the pre-trained text recognition model through the voice features and a training text; dividing digital information and text information in the training text, and performing model training on a pre-trained word vector model by utilizing the preprocessed text and the training word vector; carrying out vector fusion on the training word vector, the digital information and the rule text data, and carrying out semantic association analysis on the training text and the rule text data; and generating a feature vector corresponding to the context vector and the semantic association graph, constructing a pre-trained risk discrimination model of the feature vector, and performing model training on the pre-trained risk discrimination model by using the feature vector and the training category. According to the invention, risk control voice monitoring intelligence can be improved.

The present invention discloses a speech data training method and recognition method based on a distributed array, wherein the training method includes: determining each microphone array speech data and its optimal microphone array label signal; inputting each microphone array speech data and the optimal microphone array label signal into a neural network model for training, extracting microphone phase features of two different channels and energy features of microphone speech data of any channel for splicing; and gradually optimizing the neural network model until it outputs the optimal speech signal. The present invention uses a distributed array speech data training method, utilizes a neural network model to train and output the optimal speech signal of the distributed microphone array speech data, can accurately identify the speech signal of the optimal sound source, and recognizes the training results based on a multi-channel attention feature fusion network model, significantly improves the speech recognition performance, and ultimately ensures the accuracy of speech recognition, meeting the speech recognition requirements in a far-field sound pickup environment.

This application discloses a training method, apparatus, device, and storage medium for a speech recognition model, belonging to the field of artificial intelligence. The method includes: acquiring a sample audio set, the sample audio set including multiple sample audios; filtering candidate sample audios from the sample audio set based on an initial speech recognition model; extracting audio segments from the candidate sample audios; wherein the audio segments include audios aligned with consecutive identical text units from the candidate sample audios; and the initial speech recognition model, when performing speech recognition on the candidate sample audios, failed to correctly recognize the consecutive identical text units; and retraining the initial speech recognition model based on the audio segments to obtain a target speech recognition model. This application can improve speech recognition quality, particularly improving the accuracy of recognizing consecutive identical text units.