Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating and improving live translation captioning systems. An exemplary method includes: displaying a word in a first language; receiving a first audio sequence, the first audio sequence comprising a verbal description of the word; generating a first translated text in a second language; displaying the first translated text; receiving a second audio sequence, the second audio sequence comprising a guessed word based on the first translated text; generating a second translated text in the first language; determining a matching score between the word and the second translated text; determining a performance score of the live translation captioning system based on the matching score.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating and improving live translation captioning systems. An exemplary method includes: displaying a word in a first language; receiving a first audio sequence, the first audio sequence comprising a verbal description of the word; generating a first translated text in a second language; displaying the first translated text; receiving a second audio sequence, the second audio sequence comprising a guessed word based on the first translated text; generating a second translated text in the first language; determining a matching score between the word and the second translated text; determining a performance score of the live translation captioning system based on the matching score.

A system, method, and computer program product for implementing voice activated hardware device enabling is provided. The method includes receiving an instruction set comprising initialization commands associated with initializing a non-specific device of a plurality of devices. The instruction set is analyzed and it is determined that specified instruction keywords are located within the initialization commands. The initialization commands are inputted into a bidirectional encoder representations from transformers (BERT) model classifier component and a specified device associated with the instruction set is classified. As a result, it is determined if a maximum threshold is reached for any class of devices and the specified device is assigned as a desired device. In response, the specified device is enabled with respect to an operationally functional state.

A system, method, and computer program product for implementing voice activated hardware device enabling is provided. The method includes receiving an instruction set comprising initialization commands associated with initializing a non-specific device of a plurality of devices. The instruction set is analyzed and it is determined that specified instruction keywords are located within the initialization commands. The initialization commands are inputted into a bidirectional encoder representations from transformers (BERT) model classifier component and a specified device associated with the instruction set is classified. As a result, it is determined if a maximum threshold is reached for any class of devices and the specified device is assigned as a desired device. In response, the specified device is enabled with respect to an operationally functional state.

A method for providing sign language is disclosed. The method includes receiving, by an electronic device, a natural language information input from at least one source for conversion into sign language. The natural language information input includes at least one sentence. The method further includes predicting, by the electronic device, an emphasis score for each word of the at least one sentence based on acoustic components. The method further includes rephrasing, by the electronic device, the at least one sentence based on the emphasis score of each of the words. The method further includes converting, by the electronic device, the at least one rephrased sentence into the sign language. The method further includes delivering, by the electronic device, the sign language.

A method for providing sign language is disclosed. The method includes receiving, by an electronic device, a natural language information input from at least one source for conversion into sign language. The natural language information input includes at least one sentence. The method further includes predicting, by the electronic device, an emphasis score for each word of the at least one sentence based on acoustic components. The method further includes rephrasing, by the electronic device, the at least one sentence based on the emphasis score of each of the words. The method further includes converting, by the electronic device, the at least one rephrased sentence into the sign language. The method further includes delivering, by the electronic device, the sign language.

An audio recognition method, including: acquiring an audio file to be recognized (S100); extracting audio feature information of the audio file to be recognized, the audio feature information including audio fingerprints (S200); searching, in a fingerprint index database, audio attribute information matched with the audio feature information, the fingerprint index database including an audio fingerprint set in which invalid audio fingerprint removal has been performed on audio sample data (S300). As the audio fingerprint set in the fingerprint index database has been subjected to invalid audio fingerprint removal of audio sample data, the storage space of audio fingerprints in the fingerprint index database can be reduced, and the audio recognition efficiency can be improved. Further provided are an audio recognition device and a server.

An audio recognition method, including: acquiring an audio file to be recognized (S100); extracting audio feature information of the audio file to be recognized, the audio feature information including audio fingerprints (S200); searching, in a fingerprint index database, audio attribute information matched with the audio feature information, the fingerprint index database including an audio fingerprint set in which invalid audio fingerprint removal has been performed on audio sample data (S300). As the audio fingerprint set in the fingerprint index database has been subjected to invalid audio fingerprint removal of audio sample data, the storage space of audio fingerprints in the fingerprint index database can be reduced, and the audio recognition efficiency can be improved. Further provided are an audio recognition device and a server.

In various embodiments, a quality inference application estimates perceived audio quality. The quality inference application computes a set of feature values for a set of audio features based on an audio clip. The quality inference application then uses a trained multitask learning model to generate predicted labels based on the set of feature values. The predicted labels specify metric values for metrics that are relevant to audio quality. Subsequently, the quality inference application computes an audio quality score for the audio clip based on the predicted labels.

In various embodiments, a quality inference application estimates perceived audio quality. The quality inference application computes a set of feature values for a set of audio features based on an audio clip. The quality inference application then uses a trained multitask learning model to generate predicted labels based on the set of feature values. The predicted labels specify metric values for metrics that are relevant to audio quality. Subsequently, the quality inference application computes an audio quality score for the audio clip based on the predicted labels.