Abstract: A radiation therapy system is disclosed. The radiation therapy system includes a gantry, a first radiation source, and a second radiation source. The gantry is configured to have a cavity extending in a direction along a rotation axis, and the cavity is configured to house a target object. The first radiation source is mounted on the gantry, and configured to emit a treatment beam to a treatment area of the target object. The second radiation source is mounted on the gantry, and configured to emit an imaging beam to an imaging area of the target object. The treatment area partially overlaps the imaging area. A rotation plane of the first radiation source and a rotation plane of the second radiation source are distributed in a direction along the rotation axis.

Abstract: The present disclosure provides a method for training a linguistic model, related to fields of speech, natural language processing, deep learning technologies. A method includes: obtaining grammars corresponding to a plurality of sample texts and a slot value of a slot in each grammar by using semantic analysis; generating a grammar graph corresponding to each grammar based on the corresponding grammar and the slot value of the slot in the corresponding grammar; obtaining a weight of each grammar, a weight of each slot, and a weight of each slot value in each grammar graph based on the sample texts; determining at least one grammar frequency of each order based on the weight of each grammar, the weight of each slot, and the weight of each slot value in each grammar graph; and training the linguistic model based on the at least one grammar frequency of each order.

Abstract: Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.

Abstract: The present disclosure discloses a speech recognition method and apparatus, and relates to the field of speech and deep learning technologies. A specific implementation scheme involves: acquiring candidate recognition results with first N recognition scores outputted by a speech recognition model for to-be-recognized speech, N being a positive integer greater than 1; scoring the N candidate recognition results based on pronunciation similarities between candidate recognition results and pre-collected popular entities, to obtain similarity scores of the candidate recognition results; and integrating the recognition scores and the similarity scores of the candidate recognition results to determine a recognition result corresponding to the to-be-recognized speech from the N candidate recognition results. The present disclosure can improve recognition accuracy.

Abstract: 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.

Abstract: The present disclosure provides a method for training a linguistic model, related to fields of speech, natural language processing, deep learning technologies. A method includes: obtaining grammars corresponding to a plurality of sample texts and a slot value of a slot in each grammar by using semantic analysis; generating a grammar graph corresponding to each grammar based on the corresponding grammar and the slot value of the slot in the corresponding grammar; obtaining a weight of each grammar, a weight of each slot, and a weight of each slot value in each grammar graph based on the sample texts; determining at least one grammar frequency of each order based on the weight of each grammar, the weight of each slot, and the weight of each slot value in each grammar graph; and training the linguistic model based on the at least one grammar frequency of each order.

Abstract: The disclosure provides a method and an apparatus for recognizing a speech, an electronic device and a storage medium. A speech to be recognized is obtained. An acoustic feature of the speech to be recognized and a language feature of the speech to be recognized are obtained. The speech to be recognized is input to a pronunciation difference statistics to generate a differential pronunciation pair corresponding to the speech to be recognized. The text information of the speech to be recognized is generated based on the differential pronunciation pair, the acoustic feature and the language feature.

Abstract: Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.