Speech Quality Factors for Traditional and Neural-Based Low Bit Rate Vocoders

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Title: Speech Quality Factors for Traditional and Neural-Based Low Bit Rate Vocoders
Authors: Jassim, Wissam A.Skoglund, JanChinen, MichaelHines, Andrew
Permanent link: http://hdl.handle.net/10197/12212
Date: 28-May-2020
Online since: 2021-05-26T11:54:57Z
Abstract: This study compares the performances of different algorithms for coding speech at low bit rates. In addition to widely deployed traditional vocoders, a selection of recently developed generative-model-based coders at different bit rates are contrasted. Performance analysis of the coded speech is evaluated for different quality aspects: accuracy of pitch periods estimation, the word error rates for automatic speech recognition, and the influence of speaker gender and coding delays. A number of performance metrics of speech samples taken from a publicly available database were compared with subjective scores. Results from subjective quality assessment do not correlate well with existing full reference speech quality metrics. The results provide valuable insights into aspects of the speech signal that will be used to develop a novel metric to accurately predict speech quality from generative-model-based coders.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2020 IEEE
Keywords: Machine learning & statisticsSpeech quality assessmentNeural speech synthesisWaveNetLPCNetOpusVocoder
DOI: 10.1109/QoMEX48832.2020.9123109
Language: en
Status of Item: Peer reviewed
Conference Details: International Conference on Quality of Multimedia Experience (QoMEX), Dublin, Ireland, 26-28 May 2020
ISBN: 978-1-7281-5965-2
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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