DeepAI AI Chat
Log In Sign Up

Automatic Speech Summarisation: A Scoping Review

by   Dana Rezazadegan, et al.

Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literature, with no restrictions on time frame, language summarised, research method, or paper type. We reviewed a total of 110 papers out of a set of 153 found through a literature search and extracted speech features used, methods, scope, and training corpora. Most studies employ one of four speech summarisation architectures: (1) Sentence extraction and compaction; (2) Feature extraction and classification or rank-based sentence selection; (3) Sentence compression and compression summarisation; and (4) Language modelling. We also discuss the strengths and weaknesses of these different methods and speech features. Overall, supervised methods (e.g. Hidden Markov support vector machines, Ranking support vector machines, Conditional random fields) performed better than unsupervised methods. As supervised methods require manually annotated training data which can be costly, there was more interest in unsupervised methods. Recent research into unsupervised methods focusses on extending language modelling, for example by combining Uni-gram modelling with deep neural networks. Protocol registration: The protocol for this scoping review is registered at


page 8

page 13


Development of email classifier in Brazilian Portuguese using feature selection for automatic response

Automatic email categorization is an important application of text class...

A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms

Visual speech recognition aims to identify the sequence of phonemes from...

A Comparison of Neural Network Training Methods for Text Classification

We study the impact of neural networks in text classification. Our focus...

Neural Networks for Dengue Prediction: A Systematic Review

Due to a lack of treatments and universal vaccine, early forecasts of De...

Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines

Tackling pattern recognition problems in areas such as computer vision, ...

Speech Corpora Divergence Based Unsupervised Data Selection for ASR

Selecting application scenarios matching data is important for the autom...

Automated Empathy Detection for Oncology Encounters

Empathy involves understanding other people's situation, perspective, an...