Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR
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Abstract (arXiv)
The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use of hybrid DNN/HMM sequence discriminative training. This paper presents a comprehensive investigation of various combinations of acoustic features tailored to different Acoustic Models, offering suitable feature selections for each. The incorporation of Pitch features notably improved recognition performance, especially for sentence recognition tasks involving dysarthric speech. Through a systematic examination of the TORGO database, we have demonstrated the potential to enhance the performance of the state-of-the-art Factorized Time Delay Neural Network (F-TDNN) model for recognizing dysarthric speech. Our methods, implemented with the F-TDNN model, resulted in a 4.65% relative improvement in isolated word recognition and a 4.63% relative improvement in sentence recognition for dysarthric speech, compared to previous research. This improvement effectively compensates for speech variability, attributable to our deliberate selection of the number of overlapping frames between consecutive training example chunks.
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References · 10
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
- 2023Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRcited by 23
- 2022DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASRcited by 21
- 2022A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognitioncited by 15
- 2024Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizationscited by 7
- 2022Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warpingcited by 6
- 2024Children's Speech Recognition through Discrete Token Enhancementcited by 4
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