Abstract:
A method for characterizing inhalation noise within a pressurized air delivery system, the method including the steps of: generating an inhalation noise model (912, 1012) based on inhalation noise; receiving an input signal (802) that includes inhalation noise comprising at least one inhalation noise burst; comparing (810) the input signal to the noise model to obtain a similarity measure; comparing the similarity measure to at least one threshold (832, 834) to detect the at least one inhalation noise burst; and characterizing (1354, 1356) the at least one detected inhalation noise burst.
Abstract:
Un metodo para caracterizar ruido de inhalacion dentro de un sistema de distribucion de aire presurizado, el metodo incluye las etapas de: generar un modelo (912, 1012) de ruido de inhalacion basado en el ruido de inhalacion; recibir una senal de (802) de entrada que incluye ruido de inhalacion que comprende por lo menos una rafaga del ruido de inhalacion; comparar (810) la senal de entrada con el modelo de ruido para obtener una medida de similitud; comparar la medida de similitud con por lo menos un umbral (832, 834) para detectar por lo menos una rafaga de ruido de inhalacion; y caracterizar (1354, 1356) por lo menos una rafaga de ruido de inhalacion detectada.
Abstract:
A method for equalizing a speech signal generated within a pressurized air delivery system, the method including the steps of: generating an inhalation noise model ( 1152 ) based on inhalation noise; receiving an input signal ( 802 ) that includes a speech signal; and equalizing the speech signal ( 1156 ) based on the noise model.
Abstract:
A method for detecting and attenuating inhalation noise in a communication system coupled to a pressurized air delivery system, the method including the steps of: generating an inhalation noise model ( 912, 1012 ) based on inhalation noise; receiving an input signal ( 802 ) that includes inhalation noise; comparing ( 810 ) the input signal to the noise model to obtain a similarity measure; determining ( 854 ) a gain factor based on the similarity measure; and modifying ( 852 ) the input signal based on the gain factor, wherein the inhalation noise in the input signal is attenuated based on the gain factor.
Abstract:
The present invention provides a method of calculating, within the framework of a speaker dependent system, a standard filler, or garbage model, for the detection of out-of-vocabulary utterances. In particular, the method receives new training data in a speech recognition system (202); calculates statistical parameters for the new training data (204); calculates global statistical parameters based upon the statistical parameters for the new training data (206); and updates a garbage model based upon the global statistical parameters (208). This is carried out on-line while the user is enrolling the vocabulary. The garbage model described in this disclosure is preferably an average speaker model, representative of all the speech data enrolled by the user to date. Also, the garbage model is preferably obtained as a by-product of the vocabulary enrollment procedure and is similar in it characteristics and topology to all the other regular vocabulary HMMs.
Abstract:
A communication device capable of endpointing speech utterances includes a microprocessor (110) connected to communication interface circuitry (115), memory (120), audio circuitry (130), an optional keypad (140), a display (150), and a vibrator/buzzer (160). Audio circuitry (130) is connected to microphone (133) and speaker (135). Microprocessor (110) includes a speech/noise classifier and speech recognition technology. Microprocessor (110) analyzes a speech signal to determine speech waveform parameters within a speech acquisition window. Microprocessor (110) compares the speech waveform parameters to determine the start and end points of the speech utterance. Microprocessor (110) starts at a frame index based on the energy centroid of the speech utterance and analyzes the frames preceding and following the frame index to determine the endpoints. When a potential endpoint is identified, microprocessor (110) compares the cumulative energy to the total energy of the speech acquisition window to determine whether additional speech frames are present. Accordingly, gaps and pauses in the utterance will not result in an erroneous endpoint determination.
Abstract:
A communication device capable of endpointing speech utterances includes a speech/noise classifier and speech recognition technology. A speech signal is analysed to determine speech waveform parameters within a speech acquisition window 215. The speech waveform parameters are compared to determine the start and end points of the speech utterance. Processing starts at a frame index based on the energy centroid of the speech utterance and analyzes the frames preceding and following the frame index to determine the endpoints. When a potential endpoint is identified, the cumulative energy is compared to the total energy of the speech acquisition window to determine whether additional speech frames are present 255,280. Accordingly, gaps and pauses in the utterance will not result in an erroneous endpoint determination.
Abstract:
Un metodo para ecualizar una senal de frecuencia vocal generada dentro un sistema de distribucion de aire presurizado, el metodo incluye los pasos de: generar un modelo de ruido de inhalacion (1152) basado en el ruido de inhalacion; recibir una senal de entrada (802) que incluye una senal de frecuencia vocal; e ecualizar la senal de frecuencia vocal (1152) sobre la base del modelo de ruido.