2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-47.2 | ||
| Paper Title | VOWEL NON-VOWEL BASED SPECTRAL WARPING AND TIME SCALE MODIFICATION FOR IMPROVEMENT IN CHILDREN’S ASR | ||
| Authors | Hemant Kathania, Aalto University, Finland; Avinash Kumar, Nayional Institute of Technology Sikkim, India; Mikko Kurimo, Aalto University, Finland | ||
| Session | SPE-47: Speech Recognition 17: Speech Adaptation and Normalization | ||
| Location | Gather.Town | ||
| Session Time: | Friday, 11 June, 11:30 - 12:15 | ||
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-ADAP] Speech Adaptation/Normalization | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Acoustic differences between children’s and adults’ speech causes the degradation in the automatic speech recognition (ASR) system performance when system trained on adults’ speech and tested on children’s speech. The key acoustic mismatch factors are formant, speaking rate, and pitch. In this paper, we proposed a linear prediction (LP) based spectral warping method by using the knowledge of vowel and non-vowel regions in speech signals to mitigate the formant frequencies differences between child and adult speakers. The proposed method gives 31% relative improvement over the baseline system. We have also investigated time scale modification using RTISILA and SOLAFS algorithms to compare with our proposed method and found that the proposed method gives better WER. Combining the proposed method with RTISILA and SOLAFS results in a further reduction of WER. The final combined system gives 49% relative improvement as compared to the baseline system | ||