Descriptive Analysis of Speech Quality in Mobile Communications: Descriptive Language Development and External Preference Mapping

Mattila VV. Descriptive Analysis of Speech Quality in Mobile Communications: Descriptive Language Development and External Preference Mapping. In: AES Convention 111. Audio Engineering Society; 2001. Paper 5455. Available from: https://aes.org/publications/elibrary-page/?id=9880

@inproceedings{Mattila2001_9880,
  author = {Mattila, Ville-Veikko},
  title = {{Descriptive Analysis of Speech Quality in Mobile Communications: Descriptive Language Development and External Preference Mapping}},
  booktitle = {AES Convention 111},
  note = {Paper 5455},
  year = {2001},
  month = nov,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=9880}
}

TY  - CPAPER
TI  - Descriptive Analysis of Speech Quality in Mobile Communications: Descriptive Language Development and External Preference Mapping
AU  - Mattila, Ville-Veikko
T2  - AES Convention 111
M1  - Paper 5455
PY  - 2001
DA  - 2001/11/06
UR  - https://aes.org/publications/elibrary-page/?id=9880
PB  - Audio Engineering Society
LA  - en
AB  - Perceptual analysis of speech quality in mobile communications was carried out by semantic differentiation and external preference mapping, and the developed attributes were mapped to overall quality judgements. A clean speech sample and a speech sample corrupted by car cabin noise from two speakers were processed by different processing chains representing, e.g., transmission of speech over real GSM networks, various standardised speech coders and speech coding with erroneous transmission channels, etc., resulting in a total of 170 samples. The perceptual characteristics of the test samples were described by 18 screened and trained subjects. The final descriptive language with 21 attributes and their rating scales were developed in panel discussions. The scaled attributes were mapped to overall quality evaluations collected from 30 screened and trained subjects by partial least-squares regression (PLSR).
ER  -