DNN-BASED SPEECH QUALITY ASSESSMENT FOR BINAURAL SIGNALS

被引：0

作者：

Reimes, Jan ^{[1
]}

机构：

[1] HEAD Acoust GmbH, D-52134 Herzogenrath, Germany

来源：

2022 INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC 2022) | 2022年

关键词：

Quality; prediction; binaural; deep neural networks; convolutional; attention; QoS; QoE; psychoacoustics;

D O I：

10.1109/IWAENC53105.2022.9914797

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Quality prediction in the scope of speech communication is still a challenging tasks even though several proprietary as well as standardized models were developed for this purpose over the past decades. Scenarios including acoustic paths were not covered adequately so far, in particular for signals that are perceived binaurally. The present work introduces a novel quality prediction approach that is based on recent deep learning techniques, but also considers important traditional aspects of psychoacoustics, auditory databases and standardized terminal testing methods.

引用

页数：5

共 50 条

[41] DNN-based Feature Transformation for Speech Recognition Using Throat Microphone
Lin, Shengke
Tsunakawa, Takashi
Nishida, Masafumi
Nishimura, Masafumi
[J]. 2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017), 2017, : 596 - 599
[42] DNN-Based Acoustic Modeling for Russian Speech Recognition Using Kaldi
Kipyatkova, Irina
Karpov, Alexey
[J]. SPEECH AND COMPUTER, 2016, 9811 : 246 - 253
[43] DNN-based speech enhancement with self-attention on feature dimension
Jiaming Cheng
Ruiyu Liang
Li Zhao
[J]. Multimedia Tools and Applications, 2020, 79 : 32449 - 32470
[44] DNN-Based Speech Synthesis: Importance of Input Features and Training Data
Lazaridis, Alexandros
Potard, Blaise
Garner, Philip N.
[J]. SPEECH AND COMPUTER (SPECOM 2015), 2015, 9319 : 193 - 200
[45] Comparing Fusion Models for DNN-Based Audiovisual Continuous Speech Recognition
Abdelaziz, Ahmed Hussen
[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2018, 26 (03) : 475 - 484
[46] ADAPTING AND CONTROLLING DNN-BASED SPEECH SYNTHESIS USING INPUT CODES
Luong, Hieu-Thi
Takaki, Shinji
Hente, Gustav Eje
Yamagishi, Junichi
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 4905 - 4909
[47] DNN-Based Feature Extraction for Conflict Intensity Estimation From Speech
Gosztolya, Gabor
Toth, Laszlo
[J]. IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (12) : 1837 - 1841
[48] An Adaptation Method in Noise Mismatch Conditions for DNN-based Speech Enhancement
Xu Si-Ying
Niu Tong
Qu Dan
Long Xing-Yan
[J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2018, 12 (10): : 4930 - 4951
[49] Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in DNN-Based Speech Enhancement
Xu, Ziyi
Strake, Maximilian
Fingscheidt, Tim
[J]. 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2019,
[50] Pre-Training of DNN-Based Speech Synthesis Based on Bidirectional Conversion between Text and Speech
Sone, Kentaro
Nakashika, Toru
[J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2019, E102D (08) : 1546 - 1553

← 1 2 3 4 5 →