A method of speaker modelling for a speaker recognition system, comprises: receiving a signal comprising a speaker's speech; and, for a plurality of frames of the signal: obtaining a spectrum of the speaker's speech; generating at least one modified spectrum, by applying effects related to a respective vocal effort; and extracting features from the spectrum of the speaker's speech and the at least one modified spectrum. The method further comprises forming at least one speech model based on the extracted features.

A device and method for adjusting speech intelligibility at an audio device is provided. The device comprises a microphone, a transmitter and a controller. The controller is configured to: determine a noise level at the microphone; select a voice tag, of a plurality of voice tags, based on the noise level, each of the plurality of voice tags associated with respective noise levels; determine an intelligibility rating of a mix of the voice tag and noise received at the microphone; and when the intelligibility rating is below a threshold intelligibility rating, enhance speech received the microphone based on the intelligibility rating prior to transmitting, at the transmitter, a signal representing intelligibility enhanced speech.

Device, system, and method of voice-based user authentication utilizing a challenge. A system includes a voice-based user-authentication unit, to authenticate a user based on a voice sample uttered by the user. A voice-related challenge generator operates to generate a voice-related challenge that induces the user to modify one or more vocal properties of the user. A reaction-to-challenge detector operates to detect a user-specific vocal modification in reaction to the voice-related challenge; by using a processor as well as an acoustic microphone, an optical microphone, or a hybrid acoustic-and-optical microphone. The voice-based user-authentication unit utilizes the user-specific vocal modification, that was detected as reaction to the voice-related challenge, as part of a user-authentication process.

A controller may be programmed to create a speech utterance set for speech recognition training by, in response to receiving data representing a neutral utterance and parameter values defining signal noise, generating data representing a Lombard effect version of the neutral utterance using a transfer function associated with the parameter values and defining distortion between neutral and Lombard effect versions of a same utterance due to the signal noise.

A voice enhancement device including an earpiece configured to be positioned in an ear canal of a user. A microcontroller is operatively coupled to the earpiece. The microcontroller is configured to selectively provide at least multitalker babble. An accelerometer is located within the earpiece and operatively coupled to the microcontroller. The accelerometer is configured to detect speech by the user and communicate with the microcontroller to provide the multitalker babble to the earpiece during the detected speech by the user. A method of making the voice enhancement device, and a method for increasing vocal loudness in a patient using the voice enhancement device are also disclosed.

A hearing system includes a hearing instrument for capturing a sound signal from an environment of the hearing instrument. The captured sound signal is processed, and the processed sound signal is output to a user of the hearing instrument. In a speech recognition step, the captured sound signal is analyzed to recognize speech intervals, in which the captured sound signal contains speech. In a speech enhancement procedure performed during recognized speech intervals, the amplitude of the processed sound signal is periodically varied according to a temporal pattern that is consistent with a stress rhythmic pattern of the user. A method for operating the hearing instrument is also provided.

In one example embodiment, audio characteristics of audio signals are adjusted by a first machine learning model to reduce effects of a facial covering and produce adjusted audio signals. The audio signals correspond to resulting voice signals produced from the facial covering affecting original voice signals. Speech characteristics are predicted for the adjusted audio signals by a second machine learning model. Transformed audio signals corresponding to the original voice signals are produced based on the adjusted audio signals and predicted speech characteristics.