Malaspina Labs (barbados), Inc.

Alexander Escott

Pierre Zakarauskas

Shawn E Stevenson

Clarence S H Chu

Alireza Kenarsari Anhari

Various implementations disclosed herein include a training module configured to determining a set of detection normalization threshold values associated with speaker dependent voiced sound pattern (VSP) detection. In some implementations, a method includes obtaining segment templates characterizing a concurrent segmentation of a first subset of a plurality of vocalization instances of a VSP, each segment template provides a stochastic characterization of how a particular portion of the VSP is vocalized by a particular speaker; generating a noisy segment matrix using a second subset of the plurality of vocalization instances of the VSP, wherein the noisy segment matrix includes one or more noisy copies of segment representations of the second subset; scoring segments from the noisy segment matrix against the segment templates; and determining detection normalization threshold values at two or more known SNR levels for at least one particular noise type based on a function of the scoring.

Speaker dependent voiced sound pattern detection thresholds

The various implementations described enable voice activity detection and/or pitch estimation for speech signal processing in, for example and without limitation, hearing aids, speech recognition and interpretation software, telephony, and various applications for smartphones and/or wearable devices. In particular, some implementations include systems, methods and/or devices operable to detect voice activity in an audible signal by determining a voice activity indicator value that is a normalized function of signal amplitudes associated with at least two sets of spectral locations associated with a candidate pitch. In some implementations, voice activity is considered detected when the voice activity indicator value breaches a threshold value. Additionally and/or alternatively, in some implementations, analysis of the audible signal provides a pitch estimate of detectable voice activity.

Spectral comb voice activity detection

Implementations include systems, methods and/or devices operable to enhance the intelligibility of a target speech signal by targeted voice model based processing of a noisy audible signal. In some implementations, an amplitude-independent voice proximity function voice model is used to attenuate signal components of a noisy audible signal that are unlikely to be associated with the target speech signal and/or accentuate the target speech signal. In some implementations, the target speech signal is identified as a near-field signal, which is detected by identifying a prominent train of glottal pulses in the noisy audible signal. Subsequently, in some implementations systems, methods and/or devices perform a form of computational auditory scene analysis by converting the noisy audible signal into a set of narrowband time-frequency units, and selectively accentuating the time-frequency units associated with the target speech signal and deemphasizing others using information derived from the identification of the glottal pulse train.

Voice signal enhancement

Implementations include systems, methods and/or devices operable to detect voice activity in an audible signal by detecting glottal pulses. The dominant frequency of a series of glottal pulses is perceived as the intonation pattern or melody of natural speech, which is also referred to as the pitch. However, as noted above, spoken communication typically occurs in the presence of noise and/or other interference. In turn, the undulation of voiced speech is masked in some portions of the frequency spectrum associated with human speech by the noise and/or other interference. In some implementations, detection of voice activity is facilitated by dividing the frequency spectrum associated with human speech into multiple sub-bands in order to identify glottal pulses that dominate the noise and/or other inference in particular sub-bands. Additionally and/or alternatively, in some implementations the analysis is furthered to provide a pitch estimate of the detected voice activity.

Voice activity detection and pitch estimation

Various implementations described herein include directional filtering of audible signals, which is provided to enable acoustic isolation and localization of a target voice source. Without limitation, various implementations are suitable for speech signal processing applications in, hearing aids, speech recognition software, voice-command responsive software and devices, telephony, and various other applications associated with mobile and non-mobile systems and devices. In particular, some implementations include systems, methods and/or devices operable to emphasize at least some of the time-frequency components of an audible signal that originate from a target direction and source, and/or deemphasizing at least some of the time-frequency components that originate from one or more other directions or sources. In some implementations, directional filtering includes applying a gain function to audible signal data received from multiple audio sensors. In some implementations, the gain function is determined from the audible signal data and target values associated with directional cues.

Directional filtering of audible signals

Implementations of systems, method and devices described herein enable enhancing the intelligibility of a target voice signal included in a noisy audible signal received by a hearing aid device or the like. In particular, in some implementations, systems, methods and devices are operable to generate a machine readable formant based codebook. In some implementations, the method includes determining whether or not a candidate codebook tuple includes a sufficient amount of new information to warrant either adding the candidate codebook tuple to the codebook or using at least a portion of the candidate codebook tuple to update an existing codebook tuple. Additionally and/or alternatively, in some implementations systems, methods and devices are operable to reconstruct a target voice signal by detecting formants in an audible signal, using the detected formants to select codebook tuples, and using the formant information in the selected codebook tuples to reconstruct the target voice signal.

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Vancouver, Canada

Alexander Escott