519 research outputs found
Integrating lexical and prosodic features for automatic paragraph segmentation
Spoken documents, such as podcasts or lectures, are a growing presence in everyday life. Being able to automatically
identify their discourse structure is an important step to understanding what a spoken document is about. Moreover,
finer-grained units, such as paragraphs, are highly desirable for presenting and analyzing spoken content. However, little
work has been done on discourse based speech segmentation below the level of broad topics. In order to examine how
discourse transitions are cued in speech, we investigate automatic paragraph segmentation of TED talks using lexical
and prosodic features. Experiments using Support Vector Machines, AdaBoost, and Neural Networks show that models
using supra-sentential prosodic features and induced cue words perform better than those based on the type of lexical
cohesion measures often used in broad topic segmentation. Moreover, combining a wide range of individually weak
lexical and prosodic predictors improves performance, and modelling contextual information using recurrent neural
networks outperforms other approaches by a large margin. Our best results come from using late fusion methods that
integrate representations generated by separate lexical and prosodic models while allowing interactions between these
features streams rather than treating them as independent information sources. Application to ASR outputs shows that
adding prosodic features, particularly using late fusion, can significantly ameliorate decreases in performance due to
transcription errors.The second author was funded from the EU’s Horizon
2020 Research and Innovation Programme under the GA
H2020-RIA-645012 and the Spanish Ministry of Economy
and Competitivity Juan de la Cierva program. The other
authors were funded by the University of Edinburgh
Improving automated segmentation of radio shows with audio embeddings
Audio features have been proven useful for increasing the performance of
automated topic segmentation systems. This study explores the novel task of
using audio embeddings for automated, topically coherent segmentation of radio
shows. We created three different audio embedding generators using multi-class
classification tasks on three datasets from different domains. We evaluate
topic segmentation performance of the audio embeddings and compare it against a
text-only baseline. We find that a set-up including audio embeddings generated
through a non-speech sound event classification task significantly outperforms
our text-only baseline by 32.3% in F1-measure. In addition, we find that
different classification tasks yield audio embeddings that vary in segmentation
performance.Comment: 5 pages, 2 figures, submitted to ICASSP202
Multimodal Content Analysis for Effective Advertisements on YouTube
The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In this work, we seek to study the characteristics or
attributes that characterize an effective advertisement and recommend a useful
set of features to aid the designing and production processes of commercial
advertisements. We analyze the temporal patterns from multimedia content of
advertisement videos including auditory, visual and textual components, and
study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an
advertisement, and to recommend a useful set of features to advertisement
designers to make it more successful and approachable to users. Our proposed
framework employs the signal processing technique of cross modality feature
learning where data streams from different components are employed to train
separate neural network models and are then fused together to learn a shared
representation. Subsequently, a neural network model trained on this joint
feature embedding representation is utilized as a classifier to predict
advertisement effectiveness. We validate our approach using subjective ratings
from a dedicated user study, the sentiment strength of online viewer comments,
and a viewer opinion metric of the ratio of the Likes and Views received by
each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201
Multimodal Assessment of Cognitive Decline: Applications in Alzheimer’s Disease and Depression
The initial diagnosis and assessment of cognitive decline are generally based around the judgement of clinicians, and commonly used semi-structured interviews, guided by pre-determined sets of topics, in a clinical set-up. Publicly available multimodal datasets have provided an opportunity to explore a range of experiments in the automatic detecting of cognitive decline. Drawing on the latest developments in representation learning, machine learning, and natural language processing, we seek to develop models capable of identifying cognitive decline with an eye to discovering the differences and commonalities that should be considered in computational treatment of mental health disorders. We present models that learn the indicators of cognitive decline from audio and visual modalities as well as lexical, syntactic, disfluency and pause information. Our study is carried out in two parts: moderation analysis and predictive modelling. We do some experiments with different fusion techniques. Our approaches are motivated by some of the recent efforts in multimodal fusion for classifying cognitive states to capture the interaction between modalities and maximise the use and combination of each modality. We create tools for detecting cognitive decline and use them to analyze three major datasets containing speech produced by people with and without cognitive decline. These findings are being used to develop multimodal models for the detection of depression and Alzheimer’s dementia
- …