Motorola Corporation

Motorola Solutions, Inc.

Google Inc.

Motorola Mobility LLC

William M Kushner

Mark A Jasiuk

Sara M Harton

Jeffrey Arthur Meunier

Yu Liu

Yi Gao

Edward Srenger

Li Xiao

Audrius Polikaitis

Tenkasi Vaideeswaran Ramabadran

Matthew A Hartman

Robert J Novorita

David John Pearce

James D Barrus

Lu-Ming Chen

Jeffrey Meunier

Embodiments of an acoustic feedback suppressor determine the energy in each of a plurality of frequency bands of frames of an audio signal. The energy in each of the plurality of frequency bands is compared to characteristic of human voice to determine that a present frame contains content that is not likely human voice and exhibits a characteristic of feedback. Upon determining that feedback is occurring, an adaptive gain reduction is applied to the band in which feedback is suspected to be occurring.

Method and apparatus for mitigating feedback in a digital radio receiver

A method and apparatus for enhancing modulation of certain speech sounds, such as trill sounds, are provided for radios which utilize digital vocoders. A digitized speech stream is sampled and the sampling is adjusted to determine, detect and enhance trill nulls in the digitized voice stream by one or more of: frame shifting the digitized speech input stream prior to vocoding, time expanding a digitized speech steam prior to vocoding, time compressing a digitized speech output stream after vocoding, and/or modulation enhancement and filtering of the a digitized speech output stream after vocoding.

Method and apparatus for enhancing the modulation index of speech sounds passed through a digital vocoder

A method for estimating acoustic noise in an environment where a mobile communication device is operating and where the acoustic noise includes nonstationary noise or speech-like noises, and wherein the environment also includes speech signals. The method includes searching for a local minimum energy over a plurality of frames using at least two reference signals including a first signal comprised of a time-sensitive current local minimum energy estimate, emin, and a second signal comprised of a time-weighted average of previous detected local energy minima, eminmean; and deciding whether the detected local energy minima of the second reference signal is a noise signal. Also, binning the detected input signal energy minima values within a plurality of histograms; and calculating a composite noise energy estimate comprised of a weighted sum of a maximum probability noise energy estimate and an expected value noise energy estimate. As such a nonstationary noise estimator is formed.

Nonstationary noise estimator (NNSE)

A method and apparatus for adaptively controlling the audio output () of a communication device () according to the noise characteristics of the receiver listening environment (). An output volume for the communication device () is set by a user (e.g., listener). The communication device () can intermittently sample ambient noise () of its environment (). A minimum signal to noise threshold can be established for audio output (). A total adjustment for the audio output () is established based on the ambient noise (), the user set output volume, and the minimum signal to noise threshold. The total adjustment is a product of a software volume adjustment () and a hardware gain adjustment (). The software volume adjustment () and the hardware gain adjustment () is adaptively applied when the communication device () outputs audio ().

Method and apparatus for multi-stage adaptive volume control

A method for removing periodic noise pulses from a continuous audio signal generated in a pressurized air delivery system includes the steps of: detecting, in a time-windowed segment of the continuous audio signal generated in the pressurized air delivery system, a plurality of the periodic noise pulses having a pulse period and being representable in the form of a plurality of signal components combined by convolution; deconvolving the plurality of signal components to generate a plurality of deconvolved signal components; and removing at least a portion of the periodic noise pulses from the time-windowed segment of the continuous audio signal using the deconvolved signal components.

Method and apparatus for removing from an audio signal periodic noise pulses representable as signals combined by convolution

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 () based on inhalation noise; receiving an input signal () that includes a speech signal; and equalizing the speech signal () based on the noise model.

Method and apparatus for equalizing a speech signal generated within a pressurized air delivery system

A method for characterizing inhalation noise within a pressurized air delivery system, the method including the steps of: generating an inhalation noise model () based on inhalation noise; receiving an input signal () that includes inhalation noise comprising at least one inhalation noise burst; comparing () the input signal to the noise model to obtain a similarity measure; comparing the similarity measure to at least one threshold () to detect the at least one inhalation noise burst; and characterizing () the at least one detected inhalation noise burst.

Method and apparatus for characterizing inhalation noise and calculating parameters based on the characterization

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 () based on inhalation noise; receiving an input signal () that includes inhalation noise; comparing () the input signal to the noise model to obtain a similarity measure; determining () a gain factor based on the similarity measure; and modifying () the input signal based on the gain factor, wherein the inhalation noise in the input signal is attenuated based on the gain factor.

Method for detecting and attenuating inhalation noise in a communication system

In a distributed speech recognition system comprising a first communication device which receives a speech input ( ), encodes data representative of the speech input, and transmits the encoded data and a second remotely-located communication device which receives the encoded data and compares the encoded data with a known data set, the device including a processor with a program which controls the processor to operate according to a method of reconstructing the speech input including the step of receiving encoded data including encoded spectral data and encoded energy data. The method further includes the step of decoding the encoded spectral data and encoded energy data to determine the spectral data and energy data. The method also includes the step of combining the spectral data and energy data to reconstruct the speech input.

Method and apparatus for speech reconstruction in a distributed speech recognition system

Quantization unit ( ) comprises evaluator ( ) and comparator ( ) in signal processing for identifying an utterance in system ( ). The evaluator ( ) weights a first intermediate result of an operation on a first set of a plurality of speech parameters ( ) differently than a second intermediate result of an operation on a second set of the plurality of speech parameters ( ) in a weighted representation of the plurality of speech parameters ( ). The comparator ( ) employs the weighted representation of the plurality of speech parameters ( ) to determine a vector index to represent the plurality of speech parameters ( ). The quantization unit ( ), in one example, can employ split vector quantization in conjunction with the weighted representation to determine a vector index to represent the plurality of speech parameters ( ).

Speech recognition using unequally-weighted subvector error measures for determining a codebook vector index to represent plural speech parameters

A communication device capable of screening speech recognizer input includes a microprocessor ( ) connected to communication interface circuitry ( ), memory ( ), audio circuitry ( ), an optional keypad ( ), a display ( ), and a vibrator/buzzer ( ). Audio circuitry ( ) is connected to microphone ( ) and speaker ( ). Microprocessor ( ) includes a speech/noise classifier and speech recognition technology. Microprocessor ( ) analyzes a speech signal to determine speech waveform parameters within a speech acquisition window. Microprocessor ( ) compares the speech waveform parameters to determine whether an error exists in the signal format of the speech signal. Microprocessor ( ) informs the user when an error exists in the signal format and instructs the user how to correct the signal format to eliminate the error.

Communication device for screening speech recognizer input

A communication device capable of endpointing speech utterances includes a microprocessor (,) connected to communication interface circuitry (,), memory (,), audio circuitry (,), an optional keypad (,), a display (,), and a vibrator/buzzer (,). Audio circuitry (,) is connected to microphone (,) and speaker (,). Microprocessor (,) includes a speech/noise classifier and speech recognition technology. Microprocessor (,) analyzes a speech signal to determine speech waveform parameters within a speech acquisition window. Microprocessor (,) compares the speech waveform parameters to determine the start and end points of the speech utterance. Microprocessor (,) 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 (,) 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.

Communication device and method for endpointing speech utterances

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 (,); calculates statistical parameters for the new training data (,); calculates global statistical parameters based upon the statistical parameters for the new training data (,); and updates a garbage model based upon the global statistical parameters (,). 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.

Method of evaluating an utterance in a speech recognition system

In a statistical based speech recognition system, one of the key issues is the selection of the Hidden Markov Model that best matches a given sequence of feature observations. The problem is usually addressed by the calculation of the maximum likelihood, ML, state sequence by means of a Viterbi or other decoder. Noise or inadequate training can produce a ML sequence associated with a Hidden Markov Model other than the correct model. The method of the present invention provides improved robustness by combining the standard ML state sequence score (416) with an additional path score (418) derived from the dynamics of the ML score as a function of time. These two scores, when combined, form a hybrid metric (420) that, when used with the decoder, optimizes selection of the correct Hidden Markov Model (422).

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Arlington Heights, IL, United States of America

William M Kushner