Abstract: Automatically generating sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. A sentence is received and keywords are extracted from the sentence. Based on the keywords, additional sentences are generated. A classifier model is applied to the generated sentences to determine a sentence that satisfies a threshold. In the situation a sentence satisfies the threshold, an intent associated with the classifier model can be invoked. In the situation the sentences fail to satisfy the classifier model, the virtual assistant can attempt to interpret the received sentence according to the most likely intent by invoking a sentence generation model fine-tuned for a particular domain, generate additional sentences with a high probability of having the same intent and fulfill the specific action defined by the intent.

Abstract: Automatically generating sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. A sentence is received and keywords are extracted from the sentence. Based on the keywords, additional sentences are generated. A classifier model is applied to the generated sentences to determine a sentence that satisfies a threshold. In the situation a sentence satisfies the threshold, an intent associated with the classifier model can be invoked. In the situation the sentences fail to satisfy the classifier model, the virtual assistant can attempt to interpret the received sentence according to the most likely intent by invoking a sentence generation model fine-tuned for a particular domain, generate additional sentences with a high probability of having the same intent and fulfill the specific action defined by the intent.

Abstract: Methods and systems for correction of a likely erroneous word in a speech transcription are disclosed. By evaluating token confidence scores of individual words or phrases, the automatic speech recognition system can replace a low-confidence score word with a substitute word or phrase. Among various approaches, neural network models can be used to generate individual confidence scores. Such word substitution can enable the speech recognition system to automatically detect and correct likely errors in transcription. Furthermore, the system can indicate the token confidence scores on a graphic user interface for labeling and dictionary enhancement.

Abstract: Methods and systems for correction of a likely erroneous word in a speech transcription are disclosed. By evaluating token confidence scores of individual words or phrases, the automatic speech recognition system can replace a low-confidence score word with a substitute word or phrase. Among various approaches, neural network models can be used to generate individual confidence scores. Such word substitution can enable the speech recognition system to automatically detect and correct likely errors in transcription. Furthermore, the system can indicate the token confidence scores on a graphic user interface for labeling and dictionary enhancement.

Abstract: Automatically generating sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. A sentence is received and keywords are extracted from the sentence. Based on the keywords, additional sentences are generated. A classifier model is applied to the generated sentences to determine a sentence that satisfies a threshold. In the situation a sentence satisfies the threshold, an intent associated with the classifier model can be invoked. In the situation the sentences fail to satisfy the classifier model, the virtual assistant can attempt to interpret the received sentence according to the most likely intent by invoking a sentence generation model fine-tuned for a particular domain, generate additional sentences with a high probability of having the same intent and fulfill the specific action defined by the intent.

Abstract: Methods and systems for correction of a likely erroneous word in a speech transcription are disclosed. By evaluating token confidence scores of individual words or phrases, the automatic speech recognition system can replace a low-confidence score word with a substitute word or phrase. Among various approaches, neural network models can be used to generate individual confidence scores. Such word substitution can enable the speech recognition system to automatically detect and correct likely errors in transcription. Furthermore, the system can indicate the token confidence scores on a graphic user interface for labeling and dictionary enhancement.