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Creating speaker independent ASR system through prosody modification based data augmentation
- Source :
- Pattern Recognition Letters. 131:213-218
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- In this paper, the effect of prosody-modification-based data augmentation is explored in the context of automatic speech recognition (ASR). The primary motive is to develop ASR systems that are less affected by speaker-dependent acoustic variations. Two factors contributing towards inter-speaker variability that are focused on in this paper are pitch and speaking-rate variations. In order to simulate such an ASR task, we have trained an ASR system on adults’ speech and tested it using speech data from adult as well as child speakers. Compared to adults’ speech test case, the recognition rates are noted to be extremely degraded when the test speech is from child speakers. The observed degradation is basically due to large differences in pitch and speaking-rate between adults’ and children’s speech. To overcome this problem, pitch and speaking-rate of the training speech are modified to create new versions of the data. The original and the modified versions are then pooled together in order to capture greater acoustic variability. The ASR system trained on augmented data is noted to be more robust towards speaker-dependent variations. Relative improvements of 11.5% and 27.0% over the baseline are obtained on decoding adults’ and children’s speech test sets, respectively.
- Subjects :
- Computer science
Speech recognition
Context (language use)
02 engineering and technology
01 natural sciences
Task (project management)
Artificial Intelligence
0103 physical sciences
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
010306 general physics
Prosody
Software
Decoding methods
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 131
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi...........91a75b9bacc5df58b692a7ba30b9479f