Abstract: Systems, apparatuses, and methods for the interpretation and routing of short text messages, such as those that might be received as part of a “chat” between a customer and a customer service representative. In some embodiments, this is achieved by constructing word “vectors” based on the text in a message, with a token corresponding to each word. The word vectors are then compared to a set of mutually orthogonal unit vectors representing the “classes” or “categories” of messages that are received and are intended to be acted upon by a person or automated process. The orthogonal class unit vectors are generated by training a machine learning model using a set of previously classified text or messages.

Abstract: An automated testing system and method for evaluating voice processing systems is provided. In one embodiment, a method includes receiving a plurality of voice command inputs and a plurality of expected responses associated with the voice command inputs. A text-to-speech engine is applied to the voice command inputs to generate test command audio files. The test command audio files are provided to a testing apparatus in communication with a voice processing system. A generated response output from the voice processing system is obtained for each of the test command audio files. The generated response is captured from the testing apparatus using a sensor to detect audio and/or visual information. The obtained generated response is compared to an expected response from the plurality of expected responses for each of the test command audio files. Based on the comparison, a test result is provided for each of the voice command inputs.

Abstract: A speech processing apparatus is provided in which, while face feature points are extracted from moving image data obtained by imaging a speaker's face, for each frame, a first generation network for generating face feature points of the corresponding frame based on speech feature data extracted from uttered speech of the speaker for each frame is generated, and whether the first generation network is appropriate is evaluated using an identification network, then, a second generation network for generating the uttered speech from a plurality of uncertain settings including at least text representing utterance content of the uttered speech and information indicating emotions included in the uttered speech, a plurality of types of fixed settings which define speech quality, and the face feature points generated by the first generation network evaluated as appropriate, is generated, and whether the second generation network is appropriate is evaluated using the identification network.

Abstract: Computer-implemented techniques are described herein for generating and utilizing a universal encoder component (UEC). The UEC maps a linguistic expression in a natural language to a language-agnostic representation of the linguistic expression. The representation is said to be agnostic with respect to language because it captures semantic content that is largely independent of the syntactic rules associated with the natural language used to compose the linguistic expression. The representations is also agnostic with respect to task because a downstream training system can leverage it to produce different kinds to machine-trained components that serve different respective tasks. The UEC facilitates the generation of downstream machine-trained models by permitting a developer to train a model based on input examples expressed in a language j?, and thereafter apply it to the interpretation of documents in language j?, with no additional training required.

Abstract: The present disclosure provides a global simultaneous interpretation method and production thereof, the method includes the following steps: receiving a calling request sent by a terminal by a smart phone, connecting the calling request, and establishing a calling connection; receiving a first voice information transmitted through the calling connection by the smart phone, and when the first voice information is identified and is determined as a non-specified language, translating the first voice information into a second voice information of a specified language; and playing the second voice information by using a speaker device by the smart phone.

Abstract: A speech interface device is configured to process user speech by storing, in volatile memory of the speech interface device, audio data that represents user speech, and inputting first audio data, of the stored audio data, to an automatic speech recognition (ASR) component of the speech interface device, determining that a criterion is satisfied, and, based on the criterion being satisfied, maintaining second audio data in the volatile memory. The ASR component may generate text data based on the first audio data, a natural language understanding (NLU) component of the speech interface device may generate NLU data based on the text data, and, if the NLU data corresponds to a recognized intent, the second audio data may be deleted. Otherwise, speech processing can be resumed by inputting the second audio data to the ASR component.