Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a large training set that encompasses many possible combinations of speech and noise types, is first designed. A DNN architecture is then employed as a nonlinear regression function to ensure a powerful modeling capability. Several techniques have also been proposed to improve the DNN-based speech enhancement system, including global variance equalization to alleviate the over-smoothing problem of the regression model, and the dropout and noise-aware training strategies to further improve the generalization capability of DNNs to unseen noise conditions. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the conventional MMSE based technique. It is also interesting to observe that the proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN model, trained with artificial synthesized data, is also effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.

A regression approach to speech enhancement based on deep neural networks

Garfinkel and Ethnomethodology

Thank you very much for reading sequence organization in interaction a primer in conversation analysis. Maybe you have knowledge that, people have look numerous times for their chosen novels like this sequence organization in interaction a primer in conversation analysis, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their laptop.

Sequence Organization In Interaction A Primer In Conversation Analysis

It has now been 50 years since the publication of Luhn’s seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and necessary system components. This article attempts to fill this void by providing a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization. We also discuss the challenges that remain open, in particular the need for language generation and deeper semantic understanding of language that would be necessary for future advances in the field. We would like to thank the anonymous reviewers, our students and Noemie Elhadad, Hongyan Jing, Julia Hirschberg, Annie Louis, Smaranda Muresan and Dragomir Radev for their helpful feedback. This paper was supported in part by the U.S. National Science Foundation (NSF) under IIS-05-34871 and CAREER 09-53445. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Full text available at: http://dx.doi.org/10.1561/1500000015

Automatic Summarization

https://hal.inria.fr/hal-00743529/document

The PASCAL CHiME speech separation and recognition challenge

Introducing the PASCAL 'CHiME' Speech Separation and Recognition Challenge

We present a new corpus designed for noise-robust speech processing research, CHiME. Our goal was to produce material which is both natural (derived from reverberant domestic environments with many simultaneous and unpredictable sound sources) and controlled (providing an enumerated range of SNRs spanning 20 dB). The corpus includes around 40 hours of background recordings from a head and torso simulator positioned in a domestic setting, and a comprehensive set of binaural impulse responses collected in the same environment. These have been used to add target utterances from the Grid speech recognition corpus into the CHiME domestic setting. Data has been mixed in a manner that produces a controlled and yet natural range of SNRs over which speech separation, enhancement and recognition algorithms can be evaluated. The paper motivates the design of the corpus, and describes the collection and post-processing of the data. We also present a set of baseline recognition results. Index Terms: Data collection, Binaural, Spatialisation

/pdf/the-chime-corpus-a-resource-and-a-challenge-for-124mhnw4u8.pdf

The CHiME corpus: a resource and a challenge for computational hearing in multisource environments.

This paper is about the development of statistical models of prosodic features to generate linguistic meta-data for spoken language. In particular, we are concerned with automatically punctuating the output of a broadcast news speech recogniser. We present a statistical finite state model that combines prosodic, linguistic and punctuation class features. Experimental results are presented using the Hub–4 Broadcast News corpus, and in the light of our results we discuss the issue of a suitable method of evaluating the present task.

Punctuation annotation using statistical prosody models.

Speech-driven assistive technology can be an attractive alternative to conventional interfaces for people with physical disabilities. However, often the lack of motor-control of the speech articulators results in disordered speech, as condition known as dysarthria. Dysarthric speakers can generally not obtain satisfactory performances with off-the-shelf automatic speech recognition (ASR) products and disordered speech ASR is an increasingly active research area. Sparseness of suitable data is a big challenge. The experiments described here use UAspeech, one of the largest dysarthric databases available, which is still easily an order of magnitude smaller than typical speech databases. This study investigates how far fundamental training and adaptation techniques developed in the LVCSR community can take us. A variety of ASR systems using maximum likelihood and MAP adaptation strategies are established with all speakers obtaining significant improvements compared to the baseline system regardless of the severity of their condition. The best systems show on average 34% relative improvement on known published results. An analysis of the correlation between intelligibility of the speaker and the type of system which would represent an optimal operating point in terms of performance shows that for severely dysarthric speakers, the exact choice of system configuration is more critical than for speakers with less disordered speech.

Heidi Christensen

Papers

The PASCAL CHiME speech separation and recognition challenge

Introducing the PASCAL 'CHiME' Speech Separation and Recognition Challenge

The CHiME corpus: a resource and a challenge for computational hearing in multisource environments.

Punctuation annotation using statistical prosody models.

A Comparative Study of Adaptive, Automatic recognition of Disordered Speech