vad   50

RT : I resigned today from the German "African Studies Association" . They hold a workshop for their 50th anniversa…
VAD  from twitter_favs
may 2019 by schmitz
Skuld hos Kronofogden - vad händer nu? (Guide till hur du ska agera)
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Skuld  hos  Kronofogden    vad  händer  nu?  (guide  till  hur  du  ska  agera) 
june 2018 by cashoo
Vad är CVV-kod på kreditkort & betalkort? [4 saker du bör veta]
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Vad  är  CVV-kod    kreditkort  &  betalkort?  [4  saker  du  bör  veta] 
may 2018 by cashoo
Vad innebär räntefria dagar på kreditkortet (och varför ska du bry dig)?
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Vad  innebär  räntefria  dagar    kreditkortet? 
may 2018 by cashoo
Vad innebär egentligen lagen om god kreditgivningssed?
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Vad  innebär  egentligen  lagen  om  god  kreditgivningssed? 
april 2018 by cashoo
mindorii/kws: An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
The reference implementation and open-source datasets of the high-quality keyword spotter and voice activity detector introduced in An End-to-End Architecture for Keyword Spotting and Voice Activity Detection.
code  e2e  aws  vad  asr  deep-learning  ctc  rnn 
april 2018 by arsyed
[1611.09405] An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
"We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Connectionist Temporal Classification loss function which allow our model to achieve high accuracy on both keyword spotting and voice activity detection without retraining. In contrast to prior voice activity detection models, our architecture does not require aligned training data and uses the same parameters as the keyword spotting model. This allows us to deploy a high quality voice activity detector with no additional memory or maintenance requirements."
papers  asr  e2e  kws  ctc  vad 
december 2017 by arsyed
RT : Furious that didn’t get up in NSW Parliament. No more want people telling me how to end my life than I want th…
VAD  from twitter
november 2017 by kcarruthers
Speaker and Noise Independent Voice Activity Detection
"Voice activity detection (VAD) in the presence of heavy, non-stationary noise is a chal-lenging problem that has attracted attention in recent years. Most modern VAD systemsrequire training on highly specialized data: either labeled mixtures of speech and noise thatare matched to the application, or, at the very least, noise data similar to that encounteredin the application. Because obtaining labeled data can be a laborious task in practicalapplications, it is desirable for a voice activity detector to be able to perform well in thepresence of any type of noise without the need for matched training data. In this paper, wepropose a VAD method based on non-negative matrix factorization. We train a universalspeech model from a corpus of clean speech but do not train a noise model. Rather, theuniversal speech model is sucient to detect the presence of speech in noisy signals. Ourexperimental results show that our technique is robust to a variety of non-stationary noisesmixed at a wide range of signal-to-noise ratios and outperforms a baseline"
papers  vad  speech  nmf  ubm  best-paper  interspeech 
april 2016 by arsyed
IEEE Xplore Abstract - Lost in segmentation: Three approaches for speech/non-speech detection in consumer-produced videos
Traditional speech/non-speech segmentation systems have been designed for specific acoustic conditions, such as broadcast news or meetings. However, little research has been done on consumer-produced audio. This type of media is constantly growing and has complex characteristics such as low quality recordings, environmental noise and overlapping sounds. This paper discusses an evaluation of three different approaches for speech/non-speech detection on consumer-produced audio. The approaches are state-of-the-art speech/non-speech detectors-one based on Gaussian Mixture Models (GMM), another on Support Vector Machines (SVM), and the last on Neural Networks (NN). Using the TRECVID MED 2012 database, we designed training/testing sets combinations to aid the understanding of what speech/non-speech detection on consumer-produced media entails and how traditional approaches to this detection performed in this domain. The results revealed that the cross-domain state-of-the-art GMM and SVM systems' tests underperformed a one-layer NN algorithm, which had 20% higher accuracy and computed audio 5 times faster.
papers  speech  fst  neural-net  vad 
march 2016 by arsyed

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