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GTC ON-DEMAND

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Abstract:
We'll describe how large data scale (over two millennia of speech data per year) and low-latency requirements have enabled and required novel approaches to several speech and language models. Our talk will cover the GPU speech recognition training pipeline, continuous feedback-based training, optimizations for training, and inference on TensorRT for ultra- low latency text-to-speech models for call centers. We will discuss accuracy and latency benchmarks for speech recognition on conversational speech, speech synthesis, data-driven dialogue systems, emotion recognition, and speech act classification. We'll also demonstrate our system running on a scaled simulated call center and show live speech recognition, synthesis, and language processing.
We'll describe how large data scale (over two millennia of speech data per year) and low-latency requirements have enabled and required novel approaches to several speech and language models. Our talk will cover the GPU speech recognition training pipeline, continuous feedback-based training, optimizations for training, and inference on TensorRT for ultra- low latency text-to-speech models for call centers. We will discuss accuracy and latency benchmarks for speech recognition on conversational speech, speech synthesis, data-driven dialogue systems, emotion recognition, and speech act classification. We'll also demonstrate our system running on a scaled simulated call center and show live speech recognition, synthesis, and language processing.  Back
 
Topics:
Data Center & Cloud Infrastructure, AI in Healthcare, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9776
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Abstract:
Gridspace uses GPU-accelerated deep learning to analyze conversational speech on phone calls. We'll outline our DNN-based approach as well as several commercial applications of call grading. Our GPU-based software stack provides a novel way to process large-scale speech data. Results from a recent case study show call grading to be as accurate as human call grading and highly scalable in production. Deep call analysis with 100% coverage has never been achieved before. Also we'll discuss how this system can be improved by training continuously without expert supervision.
Gridspace uses GPU-accelerated deep learning to analyze conversational speech on phone calls. We'll outline our DNN-based approach as well as several commercial applications of call grading. Our GPU-based software stack provides a novel way to process large-scale speech data. Results from a recent case study show call grading to be as accurate as human call grading and highly scalable in production. Deep call analysis with 100% coverage has never been achieved before. Also we'll discuss how this system can be improved by training continuously without expert supervision.  Back
 
Topics:
Finance, AI Startup, Artificial Intelligence and Deep Learning, Signal and Audio Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7360
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