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This paper summarizes the 2010 CLSP Summer Workshop on speech recognition at Johns Hopkins University. The key theme of the workshop was to improve on state-of-the-art speech recognition systems by using Segmental Conditional Random Fields (SCRFs) to integrate multiple types of information. This approach uses a state-of-the-art baseline as a springboard from which to add a suite of novel features...
In this paper, we introduce an alpha-numerical sequences extraction system (keywords, numerical fields or alpha-numerical sequences) in unconstrained handwritten documents. Contrary to most of the approaches presented in the literature, our system relies on a global handwriting line model describing two kinds of information : i) the relevant information and ii) the irrelevant information represented...
In this paper, a new information extraction system by statistical shallow parsing in unconstrained handwritten documents is introduced. Unlike classical approaches found in the literature as keyword spotting or full document recognition, our approach relies on a strong and powerful global handwriting model. A entire text line is considered as an indivisible entity and is modeled with Hidden Markov...
In this paper, we present a robust spectro-temporal feature extraction technique using autoregressive models (AR) of sub-band Hilbert envelopes. AR models of Hilbert envelopes are derived using frequency domain linear prediction (FDLP). From the sub-band Hilbert envelopes, spectral features are derived by integrating these envelopes in short-term frames and the temporal features are formed by converting...
In this paper, we present a new noise compensation technique for modulation frequency features derived from syllable length segments of subband temporal envelopes. The subband temporal envelopes are estimated using frequency domain linear prediction (FDLP). We propose a technique for noise compensation in FDLP where an estimate of the noise envelope is subtracted from the noisy speech envelope. The...
Frequency domain linear prediction (FDLP) represents an efficient technique for representing the long-term amplitude modulations (AM) of speech/audio signals using autoregressive models. For the proposed analysis technique, relatively long temporal segments (1000 ms) of the input signal are decomposed into a set of sub-bands. FDLP is applied on each sub-band to model the temporal envelopes. The residual...
We present a new feature extraction technique for phoneme recognition that uses short-term spectral envelope and modulation frequency features. These features are derived from sub-band temporal envelopes of speech estimated using frequency domain linear prediction (FDLP). While spectral envelope features are obtained by the short-term integration of the sub-band envelopes, the modulation frequency...
The use of remote sensing is more and more incontrovertible in volcanic monitoring, especially in INSAR and thermal studies. A comprehensive database of high-resolution multispectral and multitemporal optical satellite imagery exists for Piton de la Fournaise, the active volcano on La Reunion Island. This database, however, remains relatively underexploited in volcanological studies of Piton de la...
Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are much shorter than typical room impulse responses. We present a feature extraction technique based on modeling temporal envelopes of the speech signal...
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