11 results about "Voice Training" patented technology

Methods implemented in a system utilizing computing programs for a speaker and a listener in conversation are provided. Aspects include (i) a reminder provisioner for a speaker which is triggered according to speed, pitch or volume of the speaker's speech, (ii) a speech training provisioner for a speaker, and (iii) an application which records and plays back difficult conversation to understand.

This application discloses a model training method, a voice wake-up method, a device, an apparatus, and a medium. The model training method includes: extracting features from acquired voice training samples to obtain first audio features of the voice training samples; using the first audio features, training at least two wake-up sub-models, each including an encoding network and a decoding network; wherein the at least two encoding networks have different parameter counts, and all decoding networks have the same model structure and parameter counts; using the first audio features, jointly training the constructed initial wake-up model to obtain a voice wake-up model; wherein the initial wake-up model includes a joint decoding network and the encoding networks in the at least two wake-up sub-models; the initial model parameters of the joint decoding network are initialized based on the model parameters of the decoding networks in the at least two wake-up sub-models; the voice wake-up model is used to recognize user audio data to wake up electronic devices.

Embodiments of the present specification provide a speech processing model training method, a speech processing method and a speech translation method. The speech processing model training method comprises: determining first speech training data corresponding to a speech processing task and second speech training data corresponding to a speech processing subtask, wherein the speech processing subtask is a subtask of the speech processing task; training a speech processing network layer in an initial speech processing model according to the second speech training data to obtain a trained initial speech processing model, wherein the speech processing network layer is related to the speech processing subtask; and performing model training on the trained initial speech processing model according to the first speech training data to obtain a target speech processing model.

The purpose of this disclosure is to provide a speech training noise enhancement system and method based on a hybrid noise generation model, comprising: an input module, a noise environment enhancement module, a speech noise enhancement module, and an output module; wherein, the input module is used to acquire simplified description information of the noise environment and clean speech data to be enhanced; the noise environment enhancement module converts the simplified description information of the noise environment into a structured noise event sequence with temporal features; the speech noise enhancement module generates multi-source hybrid noise according to the noise event sequence and adds the multi-source hybrid noise to the clean speech data according to preset rules to obtain noisy speech data; the output module is used to output the noisy speech data for use in speech model training. This disclosure fully integrates the interactive features of superposition, cancellation, and interference of different noise sources in the time dimension, solving the technical bottleneck that traditional mixing methods can only achieve linear superposition.

1. Name of the product in this design: Multifunctional Breathing and Voice Training Device. 2. Purpose of this design: For breathing training. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application discloses a kind of multi-dimension speech training plan generation method and system.The method first collects patient age, gender and speech sample, obtains seven-dimensional evaluation parameters including sound pressure, amplitude perturbation, maximum vocalization duration, fundamental frequency perturbation, vowel space area, tone impairment and speech comprehension score.Subsequently, according to the preset logic, these parameters are sequentially determined based on the parameters, and dynamically combine different training modules such as loudness, breath, pitch, vowel, glide, tone and consonant into personalized speech training plan.The application solves the problem of existing technology training scheme solidification through multi-dimensional evaluation and dynamic module matching, significantly improves the individualization degree and rehabilitation effect of speech training.

Embodiments of the present specification provide a speech processing method and a speech processing model training method. The speech processing method comprises: determining a speech to be translated, wherein the speech to be translated is real-time determined speech data or speech segment data; inputting the speech to be translated into a target speech processing model to obtain a speech translation result corresponding to the speech to be translated, wherein the target speech processing model is obtained by model training of an initial speech processing model according to a plurality of speech segment training data, and the initial speech processing model is obtained by model training of a speech processing model according to speech training data; thereby using the target speech processing model to perform speech translation on the speech to be translated to obtain an accurate speech translation result, improving the accuracy of the speech processing result of the neural network model, and avoiding the problem that the speech translation result is inaccurate due to complex speech data.

The embodiment of the application belongs to the technical field of artificial intelligence, is applied to the field of digital medical treatment, relates to a Cantonese-Mandarin mixed speech recognition method, and comprises the following steps: adding an identifier to Cantonese text in obtained Cantonese text data to obtain extended Cantonese text data; combining obtained Mandarin audio data, Mandarin text data, Cantonese audio data and the extended Cantonese text data into a mixed speech training set, inputting the mixed speech training set into a pre-constructed deep neural network model for training to obtain a mixed speech recognition model; obtaining to-be-recognized speech data, inputting the to-be-recognized speech data into the mixed speech recognition model, and outputting a speech recognition result. The application also provides a Cantonese-Mandarin mixed speech recognition device, a computer device and a storage medium. In addition, the application also relates to the technology of blockchains, and the mixed speech training set can be stored in a blockchain. The application can distinguish whether the currently recognized speech type is Cantonese or Mandarin.

The application discloses a training method and system of a voice conversion model based on data distillation, and an application method and system. The method assembles a voice training data set and trains an original voice conversion model using the voice training data set to obtain an initial voice conversion model; assembles a voice evaluation data set, inputs the voice evaluation data set into the initial voice conversion model to obtain conversion voice data corresponding to each speaker; calculates voice conversion scores of each speaker; determines whether the voice conversion scores of each speaker meet preset conditions; if not, the voice data corresponding to the speaker is removed from the voice training data set, and the remaining voice data is used as a voice database; the training is repeated; and the initial voice conversion model and the voice database are used as a trained voice conversion model and a final voice database respectively. The voice conversion model and the voice database obtained through the method can support multiple timbre conversion and have good voice conversion effect.