According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

An audio data processing method is provided. The method includes: obtaining multi-path audio data in an environmental space, obtaining a speech data set based on the multi-path audio data, and separately generating, in a plurality of enhancement directions, enhanced speech information corresponding to the speech data set; matching a speech hidden feature in the enhanced speech information with a target matching word, and determining an enhancement direction corresponding to the enhanced speech information having a highest degree of matching with the target matching word as a target audio direction; obtaining speech spectrum features in the enhanced speech information, and obtaining, from the speech spectrum features, a speech spectrum feature in the target audio direction; and performing speech authentication on the speech hidden feature and the speech spectrum feature that are in the target audio direction based on the target matching word, to obtain a target authentication result.

An audio data processing method is provided. The method includes: obtaining multi-path audio data in an environmental space, obtaining a speech data set based on the multi-path audio data, and separately generating, in a plurality of enhancement directions, enhanced speech information corresponding to the speech data set; matching a speech hidden feature in the enhanced speech information with a target matching word, and determining an enhancement direction corresponding to the enhanced speech information having a highest degree of matching with the target matching word as a target audio direction; obtaining speech spectrum features in the enhanced speech information, and obtaining, from the speech spectrum features, a speech spectrum feature in the target audio direction; and performing speech authentication on the speech hidden feature and the speech spectrum feature that are in the target audio direction based on the target matching word, to obtain a target authentication result.

Aspects of the disclosure relate to generating test cases based on voice conversation. In some embodiments, a computing platform may receive voice data associated with an agile development meeting. Subsequently, the computing platform may identify, using a natural language processing engine, context of one or more requirements being discussed during the agile development meeting. Based on identifying the context of the one or more requirements being discussed during the agile development meeting, the computing platform may store context data into a database. Next, the computing platform may map the context data to a corresponding task item of a software development project. Thereafter, the computing platform may identify one or more test cases to be generated. Then, the computing platform may cause the identified test cases to be executed.

Aspects of the disclosure relate to generating test cases based on voice conversation. In some embodiments, a computing platform may receive voice data associated with an agile development meeting. Subsequently, the computing platform may identify, using a natural language processing engine, context of one or more requirements being discussed during the agile development meeting. Based on identifying the context of the one or more requirements being discussed during the agile development meeting, the computing platform may store context data into a database. Next, the computing platform may map the context data to a corresponding task item of a software development project. Thereafter, the computing platform may identify one or more test cases to be generated. Then, the computing platform may cause the identified test cases to be executed.

A computer-implemented method for enabling gesture-based interactions between a computer program and a user is disclosed. According to certain embodiments, the method may include initiating the computer program. The method may also include detecting that a condition has occurred. The method may also include activating a gesture-based operation mode of the computer program. The method may also include receiving gesture data generated by a sensor, the gesture data representing a gesture performed by the user. The method may further include performing a task based on the gesture data.

This disclosure describes techniques that facilitate selectively interacting with a computing resource based on receipt of an incoming voice command. Particularly, a voice control integration system may parse content of an incoming voice command to authenticate an identity of the client, and further determine an intended meaning of the incoming voice command. In doing so, the voice control integration system may interact with a computing resource to perform an action that fulfills a client request. Computing resources may be associated with service providers or client devices of a client. Further, the voice control integration system may authenticate a client identity based on a one or two-factor authentication protocol, of which one may correspond to a biometric analysis of the incoming voice command. Further, a second-factor of the two-factor authentication protocol may be implemented via a voice interaction device, another client device accessible to the client, or a combination of both.

This disclosure describes techniques that facilitate selectively interacting with a computing resource based on receipt of an incoming voice command. Particularly, a voice control integration system may parse content of an incoming voice command to authenticate an identity of the client, and further determine an intended meaning of the incoming voice command. In doing so, the voice control integration system may interact with a computing resource to perform an action that fulfills a client request. Computing resources may be associated with service providers or client devices of a client. Further, the voice control integration system may authenticate a client identity based on a one or two-factor authentication protocol, of which one may correspond to a biometric analysis of the incoming voice command. Further, a second-factor of the two-factor authentication protocol may be implemented via a voice interaction device, another client device accessible to the client, or a combination of both.

A system for capturing and processing portions of a spoken utterance command that may occur before a wakeword. The system buffers incoming audio and indicates locations in the audio where the utterance changes, for example when a long pause is detected. When the system detects a wakeword within a particular utterance, the system determines the most recent utterance change location prior to the wakeword and sends the audio from that location to the end of the command utterance to a server for further speech processing.

A system for capturing and processing portions of a spoken utterance command that may occur before a wakeword. The system buffers incoming audio and indicates locations in the audio where the utterance changes, for example when a long pause is detected. When the system detects a wakeword within a particular utterance, the system determines the most recent utterance change location prior to the wakeword and sends the audio from that location to the end of the command utterance to a server for further speech processing.