TY - GEN
T1 - Speech Intelligibility Enhancement by Equalization for in-Car Applications
AU - Gentet, Enguerrand
AU - David, Bertrand
AU - Denjean, Sebastien
AU - Richard, Gael
AU - Roussarie, Vincent
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase intelligibility by applying dedicated voice transformation techniques without changing the original SNR. The proposed method consists in an adaptive equalizer which reallocates the energy of frequency bands to maximize the Speech Intelligibility Index (SII) under the constraint of a fixed perceived loudness. The validation of the algorithm is carried out by means of a perceptual test derived from the Hearing in Noise Test (HINT) using four typical in-car noises of different driving conditions. The results obtained demonstrate the merit of the algorithm for low-frequency noises, that correspond to usual driving conditions, but also show the limit of the algorithm on noises with a spectrum more spread out induced by rain.
AB - In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase intelligibility by applying dedicated voice transformation techniques without changing the original SNR. The proposed method consists in an adaptive equalizer which reallocates the energy of frequency bands to maximize the Speech Intelligibility Index (SII) under the constraint of a fixed perceived loudness. The validation of the algorithm is carried out by means of a perceptual test derived from the Hearing in Noise Test (HINT) using four typical in-car noises of different driving conditions. The results obtained demonstrate the merit of the algorithm for low-frequency noises, that correspond to usual driving conditions, but also show the limit of the algorithm on noises with a spectrum more spread out induced by rain.
KW - near-end listening enhancement
KW - sentence recognition in noise
KW - speech intelligibility index
U2 - 10.1109/ICASSP40776.2020.9053537
DO - 10.1109/ICASSP40776.2020.9053537
M3 - Conference contribution
AN - SCOPUS:85089210062
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6934
EP - 6938
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -