Chief Scientist, Speech Technology and Research Laboratory (STAR)
Horacio Franco is chief scientist in the Speech Technology and Research Laboratory at SRI International. Since joining SRI in 1990, he has contributed in the areas of acoustic modeling, hybrid Hidden Markov Model (HMM)-neural net speech recognition approaches, speech recognizer architectures, speech technology for language learning, noise-robust speech recognition, and speech-to-speech translation systems.
He has co-authored more 70 papers, as well as several communications and book chapters. Franco is co-author of several U.S. patents in aspects of speech recognition, speech technology for language learning, and noise-robust speech recognition. He has also been active in leading efforts to develop and deploy SRI’s speech technology in different areas of government and commercial use.
Franco received his engineer’s degree in electronics in 1978, and his doctor in engineering degree in 1996, both from the University of Buenos Aires.
Recent publications
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Wideband Spectral Monitoring Using Deep Learning
We present a system to perform spectral monitoring of a wide band of 666.5 MHz, located within a range of 6 GHz of Radio Frequency (RF) bandwidth, using state-of-the-art deep learning approaches.
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Voices Obscured in Complex Environmental Settings (VOiCES) corpus
This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.
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Tackling Unseen Acoustic Conditions in Query-by-Example Search Using Time and Frequency Convolution for Multilingual Deep Bottleneck Features
This paper revisits two neural network architectures developed for noise and channel robust ASR, and applies them to building a state-of-art multilingual QbE system.
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Noise-robust Exemplar Matching for Rescoring Query-by-Example Search
This paper describes a two-step approach for keyword spotting task in which a query-by-example search is followed by noise robust exemplar matching rescoring.
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Leveraging Deep Neural Network Activation Entropy to Cope with Unseen Data in Speech Recognition
This work aims to estimate the propagation of such distortion in the form of network activation entropy, which is measured over a short-time running window on the activation from each neuron of a given hidden layer, and these measurements are then used to compute summary entropy.
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Joint modeling of articulatory and acoustic spaces for continuous speech recognition tasks
This paper investigates using deep neural networks (DNN) and convolutional neural networks (CNNs) for mapping speech data into its corresponding articulatory space.