The disclosure is directed to a directional keyword verification method and an electronic device using the same method. According to an exemplary embodiment, the proposed keyword verification method would include receiving an audio stream; analyzing the audio stream to obtain at least a word; determining whether the word matches a key word from a keyword database; assigning the word as a filler if the word does not match the keyword from the keyword database; determining whether a vowel pattern of the word matches the vowel pattern of the keyword if the word matches the key word from the keyword database; assigning the first word as a trigger or command word if the vowel pattern of the word matches the vowel pattern of the key word; and otherwise assigning the word as a filler if the vowel pattern of the word does not match the vowel pattern of the key word.

Proposed is a method and apparatus for determining a weighting function for quantizing a linear predictive coding (LPC) coefficient and having a low complexity. The weighting function determination apparatus may convert an LPC coefficient of a mid-subframe of an input signal to one of a immittance spectral frequency (ISF) coefficient and a line spectral frequency (LSF) coefficient, and may determine a weighting function associated with an importance of the ISF coefficient or the LSF coefficient based on the converted ISF coefficient or LSF coefficient.

A speech signal processing apparatus and a speech signal processing method for enhancing speech intelligibility are provided. The speech signal processing apparatus includes an input signal gain determiner to determine a gain of an input signal based on a harmonic characteristic of a voiced speech, a voiced speech output unit to output a voiced speech in which a harmonic component is preserved by applying the gain to the input signal, a linear predictive coefficient determiner to determine a linear predictive coefficient based on the voiced speech, and an unvoiced speech preserver to preserve an unvoiced speech of the input signal based on the linear predictive coefficient.

A speech signal processing apparatus and a speech signal processing method for enhancing speech intelligibility are provided. The speech signal processing apparatus includes an input signal gain determiner to determine a gain of an input signal based on a harmonic characteristic of a voiced speech, a voiced speech output unit to output a voiced speech in which a harmonic component is preserved by applying the gain to the input signal, a linear predictive coefficient determiner to determine a linear predictive coefficient based on the voiced speech, and an unvoiced speech preserver to preserve an unvoiced speech of the input signal based on the linear predictive coefficient.

Techniques are described for detecting a conversation between at least two people, and for reducing noise during the conversation. In certain embodiments, at least one speech metric is generated based on spectral analysis of an audio signal and is used to determine that the audio signal represents speech from a first person. Responsive to determining that the speech is part of a conversation between the first person and a second person an operating state of a device in a physical environment is adjusted such that a volume level of sound contributed by or associated with the device is reduced. The sound contributed by or associated with the device corresponds to noise, at least for the duration of the conversation. Therefore, reducing the volume level of sound contributed by or associated with the device reduces the overall noise level in the environment, resulting in a reduction in conversational effort.

A method for processing audio signals includes extracting a fundamental frequency (F0) component from a first audio signal; processing the first audio signal with Dominant Melody Enhancement (DoME) based on a hearing profile and output a second audio signal; and providing the second audio signal to the user. The DoME enhances the F0 component. The enhancement weight of the DoME is corresponding to the hearing profile.

An emotion estimation system includes a feature amount extraction unit, a vowel section specification unit, and an estimation unit. The feature amount extraction unit analyzes recorded produced speech to extract a predetermined feature amount. The vowel section specification unit specifies, based on the feature amount extracted by the feature amount extraction unit, a section in which a vowel is produced. The estimation unit estimates, based on the feature amount in a vowel section specified by the vowel section specification unit, an emotion of a speaker.

A method includes computing one or more values of at least one parameter at respective times during an exhalation of a subject, based on one or more properties of sound passing through air exhaled by the subject during the exhalation, the parameter being related to a concentration of a gas in the air. The method further includes generating an output in response to the values. Other embodiments are also described.

A method includes computing one or more values of at least one parameter at respective times during an exhalation of a subject, based on one or more properties of sound passing through air exhaled by the subject during the exhalation, the parameter being related to a concentration of a gas in the air. The method further includes generating an output in response to the values. Other embodiments are also described.

This application describes methods and apparatus for speaker recognition. An apparatus according to an embodiment has an analyzer for analyzing each frame of a sequence of frames of audio data which correspond to speech sounds uttered by a user to determine at least one characteristic of the speech sound of that frame. An assessment module determines, for each frame of audio data, a contribution indicator of the extent to which that frame of audio data should be used for speaker recognition processing based on the determined characteristic of the speech sound. Said contribution indicator comprises a weighting to be applied to each frame in the speaker recognition processing. In this way frames which correspond to speech sounds that are of most use for speaker discrimination may be emphasized and/or frames which correspond to speech sounds that are of least use for speaker discrimination may be de-emphasized.