Titlebook: Statistical Language and Speech Processing; 5th International Co Nathalie Camelin,Yannick Estève,Carlos Martín-Vide Conference proceedings

Intend

SEVER

Graph-Based Features for Automatic Online Abuse Detectiony. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational network

前兆

Exploring Temporal Analysis of Tweet Content from Cultural Eventsy publishing messages during the event itself, but also outside of this period. Word embedding has become a popular way to represent and extract information from such messages. In this paper, we propose a preliminary work aiming at assessing the benefits of taking temporal information into account w

Organonitrile

Evocative

Three Experiments on the Application of Automatic Speech Recognition in Industrial Environmentslating to the capturing device applied, the signal pre-processing employed, and the recognition engine used. Here, our aim was to create experimental conditions as close as possible to the envisioned application, i.e., an industrial adoption of ASR. Our results show the existence of evident dependen

違法事實(shí)

Enriching Confusion Networks for Post-processingword error rate and to compute confidence measures, but they are also used in many ways in order to improve post-processing of ASR outputs. For instance, they can be helpfully used to propose alternative word hypotheses when ASR outputs are corrected by a human on post-edition. However, CNs bins do

grudging

動(dòng)機(jī)

西瓜

Low Latency MaxEnt- and RNN-Based Word Sequence Models for Punctuation Restoration of?Closed Captionels were proposed in automatic punctuation exploiting wide word contexts. In real-time ASR tasks such as closed captioning of live TV streams, text based punctuation poses two particular challenges: a requirement for low latency (limiting the future context), and the propagation of ASR errors, seen

樸素

Unsupervised Speech Unit Discovery Using K-means and Neural Networksn which we use phone segmentation followed by clustering the segments together using k-means and a Convolutional Neural Network. We thus obtain an annotation of the corpus in pseudo-phones, which then allows us to find pseudo-words. We compare the results for two different segmentations: manual and