Nvidia Corporation

Nuance Communications, Inc.

Jagadeesh Balam

Sridhar Pilli

Qian-Yu Tang

Mahesh Godavarti

Jose Lainez

Nithin Rao Koluguri

Taejin Park

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker diarization. The techniques include obtaining a speaker embedding for various reference times of a speech and for various differently-sized time intervals, identifying a plurality of clusters, each cluster associated with a different speaker of the speech. The techniques further include computing, using the speaker embeddings, a set of embedding weights for various differently-sized time intervals, and identifying, using the computed set of the embedding weights, one or more speakers speaking at a respective reference time.

Multi-scale speaker diarization for conversational AI systems and applications

The present disclosure is directed towards a method for speech intelligibility. The method may include receiving, at one or more computing devices, a first speech input from a first user and performing voice activity detection upon the first speech input. The method may also include analyzing a spectral tilt associated with the first speech input, wherein analyzing includes computing an impulse response of a linear predictive coding ('LPC') synthesis filter in a linear pulse code modulation ('PCM') domain and wherein the one or more computing devices includes an adaptive high pass filter configured to recalculate one or more linear prediction coefficients.

System and method for speech enhancement on compressed speech

Growing community of inventors

Campbell, CA, United States of America

Jagadeesh Balam