Co dělat a nedělat, pokud přemýšlíte o založení podnikání? Je cashflow sprosté slovo? Najde si každý produkt skutečně svého zákazníka?
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This talk will discuss present and future applications of ASRU technologies. The talk will include analysis of the state of the art and will provide insights into future directions for research and ap...
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This talk will summarize our experience in developing speech-to-text transcription systems with little or no manually transcribed data and limited resources (lexicon or language specific knowledge).
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In this talk I'll give an overview of speech at google, its history, the products where speech is a central component, our existing API's, and how these products and API's have evolved over time.
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This presentation will describe the data resources collected to support the Babel program, the challenges that performers have faced in the program when working with this data in the Base Period of th...
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Several use cases how speech technologies (mainly speech transcription are used in call centers, banks, by governmental agencies, or by broad cast service providers for speech data mining, voice analy...
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Selected poster summaries- pannel discussion at ASRU 2013.
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Selected poster summaries- pannel discussion at ASRU 2013.
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The development of an automatic speech recognizer is typically a highly supervised process involving the specification of phonetic inventories, lexicons, acoustic and language models, along with annot...
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Hidden Markov models (HMMs) have been applied to the problem of automatic speech recognition for more than 40 years and today HMMs are used in nearly all commercial and research speech recognition sys...
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This presentation will describe the long journey from early speech classification experiments with MLPs in the 1960s to the present day implementations.
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This is arguably the largest gain obtained through a single technology in ASR. This talk will describe how this discovery has been further developed towards use in practical systems.
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Closing discussion ASRU 2013 What´s wrong with ASR and what can we do about it.
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Automatic pattern classifiers that output soft, probabilistic classifications can be more widely and more profitably applied, provided the probabilistic output is well-calibrated.
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Large-scale data resources are currently available for a minority of languages and the costs for data collections are prohibitive to all but the most widely spoken and economically viable languages.
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ASRU 2013 closing and Best poster prize. The ASRU workshop meets every two years and has a tradition of bringing together researchers from academia and industry in an intimate and collegial setting to...
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This presentation will show how speech recognition, natural language processing, and information retrieval techniques are being combined to drive an innovative anticipatory search engine.
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ASRU 2013 opening, announcing best paper and best student paper prizes.
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Speech production is a highly complex sensorimotor task involving tightly coordinated processing in the frontal, temporal, and parietal lobes of the cerebral cortex.
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Multimedia content over the Internet is very attractive, while the spoken part of such content very often tells the core information. It is therefore possible to index, retrieve or browse multimedia c...
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Speech synthesis is often regarded as a messy problem. This talk will discuss how we can formulate the problem of speech synthesis in a statistical machine learning framework.
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The robustness of speech recognition systems to acoustic variability is a key factor to their success or failure.
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Object recognition is a little like translation: a picture (text in a source language) goes in, and a description (text in a target language) comes out. I will use this analogy, which has proven ferti...
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