Malaspina Labs (barbados), Inc.

Alireza Kenarsari Anhari

Pierre Zakarauskas

Alexander Escott

Clarence Chu

Clarence S H Chu

Shawn E Stevenson

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

Various implementations disclosed herein include a training module configured to produce a set of segment templates from a concurrent segmentation of a plurality of vocalization instances of a VSP vocalized by a particular speaker, who is identifiable by a corresponding set of vocal characteristics. Each segment template provides a stochastic characterization of how each of one or more portions of a VSP is vocalized by the particular speaker in accordance with the corresponding set of vocal characteristics. Additionally, in various implementations, the training module includes systems, methods and/or devices configured to produce a set of VSP segment maps that each provide a quantitative characterization of how respective segments of the plurality of vocalization instances vary in relation to a corresponding one of a set of segment templates.

Speaker dependent voiced sound pattern template mapping

Various implementations disclosed herein include a training module configured to concurrently segment a plurality of vocalization instances of a voiced sound pattern (VSP) as vocalized by a particular speaker, who is identifiable by a corresponding set of vocal characteristics. Aspects of various implementations are used to determine a concurrent segmentation of multiple similar instances of a VSP using a modified hierarchical agglomerative clustering (HAC) process adapted to jointly and simultaneously segment multiple similar instances of the VSP. Information produced from multiple instances of a VSP vocalized by a particular speaker characterize how the particular speaker vocalizes the VSP and how those vocalizations may vary between instances. In turn, in some implementations, the information produced using the modified HAC process is sufficient to determine more a reliable detection (and/or matching) threshold metric(s) for detecting and matching the VSP as vocalized by the particular speaker.

Concurrent segmentation of multiple similar vocalizations

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.

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Vancouver, Canada

Alireza Kenarsari Anhari