Abstract: Devices, systems, methods, media, and programs for detecting an emotional state change in an audio signal are provided. A plurality of segments of the audio signal is received, with the plurality of segments being sequential. Each segment of the plurality of segments is analyzed, and, for each segment, an emotional state and a confidence score of the emotional state are determined. The emotional state and the confidence score of each segment are sequentially analyzed, and a current emotional state of the audio signal is tracked throughout each of the plurality of segments. For each segment, it is determined whether the current emotional state of the audio signal changes to another emotional state based on the emotional state and the confidence score of the segment.

Abstract: A system that incorporates teachings of the subject disclosure may include, for example, a method for controlling a steering of a plurality of cameras to identify a plurality of potential sources, identifying the plurality of potential sources according to image data provided by the plurality of cameras, assigning a beam of a plurality of beams of a plurality of microphones to each of the plurality of potential sources, detecting a first command comprising one of a first audible cue based on signals from a portion of the plurality of microphones, a first visual cue based on image data from one of the plurality of cameras, or both for controlling a media center, and configuring the media center according to the first command. Other embodiments are disclosed.

Abstract: Concepts and technologies are disclosed herein for providing navigation routes and/or providing navigation route updates. According to various embodiments of the concepts and technologies disclosed herein, a navigation application can be configured to obtain route data from a routing service. The routing service can be configured to use navigation data locally stored and/or obtained from a number of sources to generate navigation routes and/or to update navigation routes. The generated and/or updated navigation routes can be provided to the user device as route data that can be used to provide navigation directions to a user.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for combining frame and segment level processing, via temporal pooling, for phonetic classification. A frame processor unit receives an input and extracts the time-dependent features from the input. A plurality of pooling interface units generates a plurality of feature vectors based on pooling the time-dependent features and selecting a plurality of time-dependent features according to a plurality of selection strategies. Next, a plurality of segmental classification units generates scores for the feature vectors. Each segmental classification unit (SCU) can be dedicated to a specific pooling interface unit (PIU) to form a PIU-SCU combination. Multiple PIU-SCU combinations can be further combined to form an ensemble of combinations, and the ensemble can be diversified by varying the pooling operations used by the PIU-SCU combinations.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for generating an acoustic model for use in speech recognition. A system configured to practice the method first receives training data and identifies non-contextual lexical-level features in the training data. Then the system infers sentence-level features from the training data and generates a set of decision trees by node-splitting based on the non-contextual lexical-level features and the sentence-level features. The system decorrelates training vectors, based on the training data, for each decision tree in the set of decision trees to approximate full-covariance Gaussian models, and then can train an acoustic model for use in speech recognition based on the training data, the set of decision trees, and the training vectors.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for building an automatic speech recognition system through an Internet API. A network-based automatic speech recognition server configured to practice the method receives feature streams, transcriptions, and parameter values as inputs from a network client independent of knowledge of internal operations of the server. The server processes the inputs to train an acoustic model and a language model, and transmits the acoustic model and the language model to the network client. The server can also generate a log describing the processing and transmit the log to the client. On the server side, a human expert can intervene to modify how the server processes the inputs. The inputs can include an additional feature stream generated from speech by algorithms in the client's proprietary feature extraction.