Hierarchical Network with Label Embedding for Contextual Emotion Recognition
نویسندگان
چکیده
منابع مشابه
Label embedding for text recognition
The standard approach to recognizing text in images consists in first classifying local image regions into candidate characters and then combining them with high-level word models such as conditional random fields (CRF). This paper explores a new paradigm that departs from this bottom-up view. In our approach, every label from a lexicon is embedded to an Euclidean vector space. We refer to this...
متن کاملRODRIGUEZ-SERRANO, PERRONNIN: LABEL EMBEDDING FOR TEXT RECOGNITION 1 Label embedding for text recognition
The standard approach to recognizing text in images consists in first classifying local image regions into candidate characters and then combining them with high-level word models such as conditional random fields (CRF). This paper explores a new paradigm that departs from this bottom-up view. We propose to embed word labels and word images into a common Euclidean space. Given a word image to b...
متن کاملActive learning by label uncertainty for acoustic emotion recognition
Speech data is in principle available in large amounts for the training of acoustic emotion recognisers. However, emotional labelling is usually not given and the distribution is heavily unbalanced, as most data is ‘rather neutral’ than truly ‘emotional’. In the ‘hay stack’ of speech data, Active Learning automatically identifies the ‘needles’, i.e., the more informative instances to reduce hum...
متن کاملWord Recognition with a Hierarchical Neural Network
In this paper we propose a feedforward neural network for syllable recognition. The core of the recognition system is based on a hierarchical architecture initially developed for visual object recognition. We show that, given the similarities between the primary auditory and visual cortexes, such a system can successfully be used for speech recognition. Syllables are used as basic units for the...
متن کاملMulti-class and hierarchical SVMs for emotion recognition
This paper extends binary support vector machines to multiclass classification for recognising emotions from speech. We apply two standard schemes (one-versus-one and one-versusrest) and two schemes that form a hierarchy of classifiers each making a distinct binary decision about class membership, on three publicly-available databases. Using the OpenEAR toolkit to extract more than 6000 feature...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Research
سال: 2021
ISSN: 2639-5274
DOI: 10.34133/2021/3067943