Abstract: A method comprising: receiving an interactive voice response (IVR) tree configured to implement one or more tasks, each associated with one or more IVR node paths comprising a plurality of IVR nodes arranged in a hierarchical relationship; analyzing the IVR tree to identify one or more intent IVR nodes, each associated with one of the tasks; with respect to each of the intent IVR nodes, identifying a plurality of corresponding entity IVR nodes included within the IVR node path associated with the intent IVR node; assembling one or more task-specific chatbot skills, each comprising (i) one of the intent IVR nodes, and (ii) at least some of the plurality of corresponding entity IVR nodes, wherein each of the task-specific chatbot skills is configured to perform one of the tasks by conducting a dialog with a user; and generating a chatbot comprising at least one of the task-specific chatbot skills.

Abstract: A method comprising: receiving an interactive voice response (IVR) tree configured to implement one or more tasks, each associated with one or more IVR node paths comprising a plurality of IVR nodes arranged in a hierarchical relationship; analyzing the IVR tree to identify one or more intent IVR nodes, each associated with one of the tasks; with respect to each of the intent IVR nodes, identifying a plurality of corresponding entity IVR nodes included within the IVR node path associated with the intent IVR node; assembling one or more task-specific chatbot skills, each comprising (i) one of the intent IVR nodes, and (ii) at least some of the plurality of corresponding entity IVR nodes, wherein each of the task-specific chatbot skills is configured to perform one of the tasks by conducting a dialog with a user; and generating a chatbot comprising at least one of the task-specific chatbot skills.

Abstract: A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly, wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the a

Abstract: A method of detecting an occurrence of splicing in a speech signal includes comparing one or more discontinuities in the test speech signal to one or more reference speech signals corresponding to the test speech signal. The method may further include calculating a frame-based spectral-like representation ST of the speech signal, and calculating a frame-based spectral-like representation SE of a reference speech signal corresponding to the speech signal. The method further includes aligning ST and SE in time and frequency, calculating a distance function associated with aligned ST and SE, and evaluating the distance function to determine a score. The method also includes comparing the score to a threshold to detect if splicing occurs in the speech signal.

Abstract: A computer-implemented method, computerized apparatus and computer program product. The method comprises capturing one or more images of a scene in which a driver is driving a vehicle; analyzing the images to retrieve an event or detail; conveying to the driver the a question or a challenge related to the event or detail; receiving a response from the driver; analyzing the response; and determining a score related to the driver.

Abstract: Text-to-speech synthesis performed by deriving from a voice dataset a sequence of speech frames corresponding to a text, wherein any of the speech frames is represented in the voice dataset by a parameterized vocal tract component, glottal pulse parameters, and an aspiration noise level, transforming the speech frames in the sequence by applying a voice transformation to any of the parameterized vocal tract component, glottal pulse parameters, and aspiration noise level representing the speech frames, wherein the voice transformation is applied in accordance with a virtual voice specification that includes at least one voice control parameter indicating a value for at least one of timbre, glottal tension and breathiness, and producing a digital audio signal of synthesized speech from the transformed sequence of speech frames.

Abstract: A computer-implemented method, computerized apparatus and computer program product. The method comprises capturing one or more images of a scene in which a driver is driving a vehicle; analyzing the images to retrieve an event or detail; conveying to the driver the a question or a challenge related to the event or detail; receiving a response from the driver; analyzing the response; and determining a score related to the driver.

Abstract: A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly, wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the a

Abstract: A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly; wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the a

Abstract: A method and system are provided for text-to-speech synthesis with personalized voice. The method includes receiving an incidental audio input (403) of speech in the form of an audio communication from an input speaker (401) and generating a voice dataset (404) for the input speaker (401). The method includes receiving a text input (411) at the same device as the audio input (403) and synthesizing (312) the text from the text input (411) to synthesized speech including using the voice dataset (404) to personalize the synthesized speech to sound like the input speaker (401). In addition, the method includes analyzing (316) the text for expression and adding the expression (315) to the synthesized speech. The audio communication may be part of a video communication (453) and the audio input (403) may have an associated visual input (455) of an image of the input speaker.

Abstract: Techniques disclosed herein include systems and methods for voice-enabled searching. Techniques include a co-occurrence based approach to improve accuracy of the 1-best hypothesis for non-phrase voice queries, as well as for phrased voice queries. A co-occurrence model is used in addition to a statistical natural language model and acoustic model to recognize spoken queries, such as spoken queries for searching a search engine. Given an utterance and an associated list of automated speech recognition n-best hypotheses, the system rescores the different hypotheses using co-occurrence information. For each hypothesis, the system estimates a frequency of co-occurrence within web documents. Combined scores from a speech recognizer and a co-occurrence engine can be combined to select a best hypothesis with a lower word error rate.

Abstract: A method of detecting an occurrence of splicing in a speech signal includes comparing one or more discontinuities in the test speech signal to one or more reference speech signals corresponding to the test speech signal. The method may further include calculating a frame-based spectral-like representation ST of the speech signal, and calculating a frame-based spectral-like representation SE of a reference speech signal corresponding to the speech signal. The method further includes aligning ST and SE in time and frequency, calculating a distance function associated with aligned ST and SE, and evaluating the distance function to determine a score. The method also includes comparing the score to a threshold to detect if splicing occurs in the speech signal.

Abstract: Methods, apparatus and computer program products implement embodiments of the present invention that include receiving a time domain voice signal, and extracting a single pitch cycle from the received signal. The extracted single pitch cycle is transformed to a frequency domain, and the misclassified roots of the frequency domain are identified and corrected. Using the corrected roots, an indication of a maximum phase of the frequency domain is generated.

Abstract: A method comprising using at least one hardware processor for: providing a set of development supervectors representing features of biometric samples of multiple subjects, the biometric samples being of at least a first and a second different biometric modalities; providing at least a first and a second enrollment supervectors representing features of at least a first and a second enrollment biometric samples of a target subject correspondingly, wherein the at least first and second enrollment samples are of the at least first and the second different biometric modalities correspondingly; providing at least a first and a second verification supervectors representing features of at least a first and a second verification biometric samples of the target subject correspondingly, wherein the at least first and second verification samples are of the at least first and second different biometric modalities correspondingly; concatenating the development supervectors to a set of development generic supervector, the a

Abstract: A method and system are provided for text-to-speech synthesis with personalized voice. The method includes receiving an incidental audio input (403) of speech in the form of an audio communication from an input speaker (401) and generating a voice dataset (404) for the input speaker (401). The method includes receiving a text input (411) at the same device as the audio input (403) and synthesizing (312) the text from the text input (411) to synthesized speech including using the voice dataset (404) to personalize the synthesized speech to sound like the input speaker (401). In addition, the method includes analyzing (316) the text for expression and adding the expression (315) to the synthesized speech. The audio communication may be part of a video communication (453) and the audio input (403) may have an associated visual input (455) of an image of the input speaker.

Abstract: Method, system, and computer program product for voice transformation are provided. The method includes transforming a source speech using transformation parameters, and encoding information on the transformation parameters in an output speech using steganography, wherein the source speech can be reconstructed using the output speech and the information on the transformation parameters. A method for reconstructing voice transformation is also provided including: receiving an output speech of a voice transformation system wherein the output speech is transformed speech which has encoded information on the transformation parameters using steganography; extracting the information on the transformation parameters; and carrying out an inverse transformation of the output speech to obtain an approximation of an original source speech.

Abstract: A method and system are provided for text-to-speech synthesis with personalized voice. The method includes receiving an incidental audio input (403) of speech in the form of an audio communication from an input speaker (401) and generating a voice dataset (404) for the input speaker (401). The method includes receiving a text input (411) at the same device as the audio input (403) and synthesizing (312) the text from the text input (411) to synthesized speech including using the voice dataset (404) to personalize the synthesized speech to sound like the input speaker (401). In addition, the method includes analyzing (316) the text for expression and adding the expression (315) to the synthesized speech. The audio communication may be part of a video communication (453) and the audio input (403) may have an associated visual input (455) of an image of the input speaker.

Abstract: A method for responding to media conference deficiencies, the method includes: monitoring, by at least one receiver, a quality of media conference signals being received by at least one receiver during the media conference; sending, in response to the monitoring, to at least an end user transmitter that transmitted the media conference signals, a quality indication representative of a quality of the received media conference signals; recording inadequately received media conference signals that were inadequately received by a certain end user receiver and participating in an activity related to a transmission, to the certain end user receiver, of the inadequately received media conference signals or of a representation of the inadequately received media conference signals.

Abstract: Techniques disclosed herein include systems and methods for voice-enabled searching. Techniques include a co-occurrence based approach to improve accuracy of the 1-best hypothesis for non-phrase voice queries, as well as for phrased voice queries. A co-occurrence model is used in addition to a statistical natural language model and acoustic model to recognize spoken queries, such as spoken queries for searching a search engine. Given an utterance and an associated list of automated speech recognition n-best hypotheses, the system rescores the different hypotheses using co-occurrence information. For each hypothesis, the system estimates a frequency of co-occurrence within web documents. Combined scores from a speech recognizer and a co-occurrence engine can be combined to select a best hypothesis with a lower word error rate.

Abstract: Techniques disclosed herein include systems and methods for voice-enabled searching. Techniques include a co-occurrence based approach to improve accuracy of the 1-best hypothesis for non-phrase voice queries, as well as for phrased voice queries. A co-occurrence model is used in addition to a statistical natural language model and acoustic model to recognize spoken queries, such as spoken queries for searching a search engine. Given an utterance and an associated list of automated speech recognition n-best hypotheses, the system rescores the different hypotheses using co-occurrence information. For each hypothesis, the system estimates a frequency of co-occurrence within web documents. Combined scores from a speech recognizer and a co-occurrence engine can be combined to select a best hypothesis with a lower word error rate.