Abstract: A hearing aid device includes a plurality of microphones which converts picked sound to audio signals, an inter-signal phase difference calculation unit which calculates a phase difference between a first audio signal and a second audio signal, a hand placement determination unit, a directivity sound pickup unit which generates an output signal, a nonlinear amplification unit which controls the signal level of the output signal acquired from the directivity sound pickup unit, and a hearing aid process control unit which, when the hand placement determination unit determines that a state where a phase difference is equal to or smaller than a first threshold value continues for a first time, controls the directivity sound pickup unit such that at least one of the sensitivity-frequency characteristic and the frequency characteristics of the audio signals becomes nondirective, and controls the nonlinear amplification unit to amplify the signal levels of the acquired audio signals.

Abstract: The present invention aims at extracting a keyword of conversation without preparations by advanced anticipation of keywords of conversation.

Abstract: Disclosed are a hearing aid device and a hearing aid method capable of controlling a directivity process or an amplification process in a hearing aid with a user's hand placement so as to match a users intuition.

Abstract: A noise robustness method operates jointly in a signal domain and a model domain. For example, energy is added in the signal domain for frequency bands where an actual noise level of an incoming signal is lower than a noise level used to train models, thus obtaining a compensated signal. Also, energy is added in the model domain for frequency bands where noise level of the incoming signal or the compensated signal is higher than the noise level used to train the models. Moreover, energy is never removed, thereby avoiding problems of higher sensitivity of energy removal to estimation errors.

Abstract: Model compression is combined with model compensation. Model compression is needed in embedded ASR to reduce the size and the computational complexity of compressed models. Model-compensation is used to adapt in real-time to changing noise environments. The present invention allows for the design of smaller ASR engines (memory consumption reduced to up to one-sixth) with reduced impact on recognition accuracy and/or robustness to noises.

Abstract: An azimuth and distance calculator calculates the relative direction and distance to the next intersection to be guided, based on information on the intersection supplied from storage for received information on an object to be guided and information on the moving histories of a user. Then, the calculator converts the relative direction into a horizontal angle and the distance to an elevation angle, and passes the angles to a stereophony generator. The stereophony generator creates output sound information having a sound image localized outside of a headphone and outputs the information to the headphone. In this manner, the user can accurately understand the distance to the object.

Abstract: The present invention aims at extracting a keyword of conversation without preparations by advanced anticipation of keywords of conversation.

Abstract: A noise robustness method operates jointly in a signal domain and a model domain. For example, energy is added in the signal domain for frequency bands where an actual noise level of an incoming signal is lower than a noise level used to train models, thus obtaining a compensated signal. Also, energy is added in the model domain for frequency bands where noise level of the incoming signal or the compensated signal is higher than the noise level used to train the models. Moreover, energy is never removed, thereby avoiding problems of higher sensitivity of energy removal to estimation errors.

Abstract: An acoustic analysis unit acoustically analyzes a first utterance by a user. A pattern by-characteristic selection unit selects a trained pattern optimal for the user's utterance from a plurality of trained patterns that are previously classified and stored every characteristic. A speaker adaptation processor determines a spectral frequency distortion coefficient for correcting difference between spectral frequencies. The difference is caused by vocal tract length of a training speaker and an input speaker. Recognition of subsequent utterances using this determination improves recognition performance of the subsequent speech sounds.

Abstract: An input speech utterance is segmented into a prefixed time length to make frames, to extract an acoustic feature parameter of each frame. The acoustic feature parameter is frequency-converted by using pluralfrequency conversion coefficients previously defined. By using all combinations of plural post-conversion feature parameters obtained by the frequency conversion and at least one standard phonemic model, to compute plural similarities or distances of between the post-conversion feature parameters of each of the frames and the standard phonemic model. A frequency converting condition for normalizing the input utterance is decided by using the pluralsimilarities or distances. By using the frequency converting condition, the input utterance is normalized. With this method, even in case there is change of the speaker making a speech utterance, the individual difference of input utterance can be corrected thereby improving the performance of speech recognition.

Abstract: An acoustic analysis unit acoustically analyzes a first utterance by a user. A pattern by-characteristic selection unit selects a trained pattern optimal for the user's utterance from a plurality of trained patterns that are previously classified and stored every characteristic. A speaker adaptation processor determines a spectral frequency distortion coefficient for correcting difference between spectral frequencies. The difference is caused by vocal tract length of a training speaker and an input speaker. Recognition of subsequent utterances using this determination improves recognition performance of the subsequent speech sounds.