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: An apparatus processes an acoustic input signal to provide an output signal with reduced noise. The apparatus weights the input signal based on a frequency-dependent weighting function. A frequency-dependent threshold function bounds the weighting function from below.

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: Examples of methods are provided for generating a multilingual codebook. According to an example method, a main language codebook and at least one additional codebook corresponding to a language different from the main language are provided. A multilingual codebook is generated from the main language codebook and the at least one additional codebook by adding a sub-set of code vectors of the at least one additional codebook to the main codebook based on distances between the code vectors of the at least one additional codebook to code vectors of the main language codebook. Systems and methods for speech recognition using the multilingual codebook and applications that use speech recognition based on the multilingual codebook are also provided.

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: The invention relates to a method for generating an adapted reference for automatic speech recognition. In a first step, recognition is performed based on a spoken utterance and a recognition result which corresponds to a currently valid reference is obtained. In a second step, the currently valid reference is adapted in accordance with the utterance in order to create an adapted reference. In a third step, the adapted reference is assessed and it is decided if the adapted reference is used for further recognition.

Abstract: The present invention relates to a method and an apparatus for a robust feature extraction for speech recognition in a noisy environment, wherein the speech signal is segmented and is characterized by spectral components. The speech signal is splitted into a number of short term spectral components in L subbands, with L=1, 2, . . . and a noise spectrum from segments that only contain noise is estimated. Then a spectral subtraction of the estimated noise spectrum from the corresponding short term spectrum is performed and a probability for each short term spectrum component to contain noise is calculated. Finally these spectral component of each short-term spectrum, having a low probability to contain speech are interpolated in order to smooth those short-term, spectra that only contain noise. With the interpolation the spectral components containing noise are interpolated by reliable spectral speech components that could be found in the neighborhood.