Abstract: In some embodiments, an audio system can be monitored by determining a sound suppression mode, and sampling information representative of an input signal and an output signal resulting from processing of the input signal in the sound suppression mode. The monitoring method can further include providing a display representative of the sampled information.

Abstract: An audio system can be controlled by a method that includes obtaining a mixture value from a user, where the mixture value has a value in a range from a first value for a first state to a second value for a second state, with the first state corresponding to a desired sound having substantially all of a first content and substantially nil amount of a second content, the second state corresponding to a desired sound having substantially nil amount of the first content and substantially all of the second content, and the mixture value being a selected one among multiple values in the range. The multiple values include an unprocessed mixture value for an unprocessed state corresponding to a desired sound having unprocessed first and second contents.

Abstract: Disclosed are embodiments for mapping, with a first trained universal function approximator, the speech representation to predicted phonological feature and phoneme class probabilities; determining expected phonological feature values based on an automatic phonetic segmentation using the expected phoneme sequence and the predicted phoneme class probabilities; and classifying, with a second trained universal function approximator different from the first trained universal function approximator, a combination of the predicted phonological feature probabilities and the expected phonological feature values to thereby detect a mispronunciation present in the sampled speech waveform and facilitate phonological feature feedback associated with the mispronunciation.

Abstract: Natural-sounding synthesized speech is obtained from pieced elemental speech units that have their super-class identities known (e.g. phoneme type), and their line spectral frequencies (LSF) set in accordance with a correlation between the desired fundamental frequency and the LSF vectors that are known for different classes in the super-class. The correlation between a fundamental frequency in a class and the corresponding LSF is obtained by, for example, analyzing the database of recorded speech of a person and, more particularly, by analyzing frames of the speech signal.

Abstract: A system and method is used to compress concatenative acoustic inventories for speech. Instead of using general purpose signal compression methods such as vector quantization, the method of the invention uses multiple properties of acoustic inventories to reduce the size of the acoustic inventories, such as the close acoustic match property and acoustic units that are labeled with sufficiently fine distinctions such that between any two phones no events occur that are substantially distinct from these two phones. The close acoustic match property is where acoustic units that share the same phone are acoustically similar at the points where these units may be concatenated. By utilizing multiple properties of acoustic units, the number of parameters per unit that are stored as LPC parameters are minimized. As a result, smaller storage devices may be used due to the reduction of the size of the storage requirements.

Abstract: Natural-sounding synthesized speech is obtained from pieced elemental speech units that have their super-class identities known (e.g. phoneme type), and their line spectral frequencies (LSF) set in accordance with a correlation between the desired fundamental frequency and the LSF vectors that are known for different classes in the super-class. The correlation between a fundamental frequency in a class and the corresponding LSF is obtained by, for example, analyzing the database of recorded speech of a person and, more particularly, by analyzing frames of the speech signal.

Abstract: Natural-sounding synthesized speech is obtained from pieced elemental speech units that have their super-class identities known (e.g. phoneme type), and their line spectral frequencies (LSF) set in accordance with a correlation between the desired fundamental frequency and the LSF vectors that are known for different classes in the super-class. The correlation between a fundamental frequency in a class and the corresponding LSF is obtained by, for example, analyzing the database of recorded speech of a person and, more particularly, by analyzing frames of the speech signal.

Abstract: Natural-sounding synthesized speech is obtained from pieced elemental speech units that have their super-class identities known (e.g. phoneme type), and their line spectral frequencies (LSF) set in accordance with a correlation between the desired fundamental frequency and the LSF vectors that are known for different classes in the super-class. The correlation between a fundamental frequency in a class and the corresponding LSF is obtained by, for example, analyzing the database of recorded speech of a person and, more particularly, by analyzing frames of the speech signal.