Abstract: Apparatuses, systems, methods, and computer program products are disclosed for techniques for speech language model training and application. An apparatus includes a processor and a memory coupled with the processor. The memory stores code that is executable by the processor to train a first speech model in a first language, the first speech model used to determine one or more characteristics of speech data that is indicative of MCI, train a second speech model for use in a second language using at least a portion of the first speech model trained in the first language, apply the second speech model trained for use in the second language to speech data for a user captured in the second language, and determine, based on output from the second speech model trained for use in the second language, an assessment of MCI for the user.

Abstract: A method for managing an interaction between a user and a driver interaction system in a vehicle, the method comprising presenting a first audio output to a user from an output device of the driver interaction system, and, while presenting the first audio output to the user, receiving sensed input at the driver interaction system, processing the sensed input including determining an emotional content of the driver, and controlling the interaction based at least in part on the emotional content of the sensed input.

Abstract: An interface customized to a detected emotional state of a user is provided. Audio signals are received from at least one microphone, the audio signals being indicative of spoken words, phrases, or commands. A wake-up word (WuW) is detected in the audio signals. An emotion is also detected in the audio signals containing the WuW. An emotion-aware processing system is configured according to the detected emotion. A voice control session is performed using the emotion-aware processing system configured according to the detected emotion.

Abstract: Embodiments of the invention relate to methods for generating a multilingual acoustic model. A main acoustic model comprising a main acoustic model having probability distribution functions and a probabilistic state sequence model including first states is provided to a processor. At least one second acoustic model including probability distribution functions and a probabilistic state sequence model including states is also provided to the processor. The processor replaces each of the probability distribution functions of the at least one second acoustic model by one of the probability distribution functions and/or each of the states of the probabilistic state sequence model of the at least one second acoustic model with the state of the probabilistic state sequence model of the main acoustic model based on a criteria set to obtain at least one modified second acoustic model. The criteria set may be a distance measurement.

Abstract: The present invention relates to a method for speech recognition of a speech signal comprising the steps of providing at least one codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, that are frequency weighted such that higher weights are assigned to entries corresponding to frequencies below a predetermined level than to entries corresponding to frequencies above the predetermined level and processing the speech signal for speech recognition comprising extracting at least one feature vector from the speech signal and matching the feature vector with the entries of the codebook.

Abstract: A system and method for detecting a refrain in an audio file having vocal components. The method and system includes generating a phonetic transcription of a portion of the audio file, analyzing the phonetic transcription and identifying a vocal segment in the generated phonetic transcription that is repeated frequently. The method and system further relate to the speech-driven selection based on similarity of detected refrain and user input.

Abstract: A system and method for detecting a refrain in an audio file having vocal components. The method and system includes generating a phonetic transcription of a portion of the audio file, analyzing the phonetic transcription and identifying a vocal segment in the generated phonetic transcription that is repeated frequently. The method and system further relate to the speech-driven selection based on similarity of detected refrain and user input.

Abstract: A system and method for detecting a refrain in an audio file having vocal components. The method and system includes generating a phonetic transcription of a portion of the audio file, analyzing the phonetic transcription and identifying a vocal segment in the generated phonetic transcription that is repeated frequently. The method and system further relate to the speech-driven selection based on similarity of detected refrain and user input.

Abstract: Embodiments of the invention relate to methods for generating a multilingual acoustic model. A main acoustic model comprising a main acoustic model having probability distribution functions and a probabilistic state sequence model including first states is provided to a processor. At least one second acoustic model including probability distribution functions and a probabilistic state sequence model including states is also provided to the processor. The processor replaces each of the probability distribution functions of the at least one second acoustic model by one of the probability distribution functions and/or each of the states of the probabilistic state sequence model of the at least one second acoustic model with the state of the probabilistic state sequence model of the main acoustic model based on a criteria set to obtain at least one modified second acoustic model. The criteria set may be a distance measurement.

Abstract: The present invention relates to a method for speech recognition of a speech signal comprising the steps of providing at least one codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, that are frequency weighted such that higher weights are assigned to entries corresponding to frequencies below a predetermined level than to entries corresponding to frequencies above the predetermined level and processing the speech signal for speech recognition comprising extracting at least one feature vector from the speech signal and matching the feature vector with the entries of the codebook.

Abstract: A system and method for detecting a refrain in an audio file having vocal components. The method and system includes generating a phonetic transcription of a portion of the audio file, analyzing the phonetic transcription and identifying a vocal segment in the generated phonetic transcription that is repeated frequently. The method and system further relate to the speech-driven selection based on similarity of detected refrain and user input.