Making more of little data: Improving low-resource automatic speech recognition using data augmentation
The performance of automatic speech recognition (ASR) systems has advanced
substantially in recent years, particularly for languages for which a large amount of …
substantially in recent years, particularly for languages for which a large amount of …
Mitigating bias against non-native accents
Automatic speech recognition (ASR) systems have seen substantial improvements in the
past decade; however, not for all speaker groups. Recent research shows that bias exists …
past decade; however, not for all speaker groups. Recent research shows that bias exists …
Accented speech recognition: Benchmarking, pre-training, and diverse data
Building inclusive speech recognition systems is a crucial step towards developing
technologies that speakers of all language varieties can use. Therefore, ASR systems must …
technologies that speakers of all language varieties can use. Therefore, ASR systems must …
Voice conversion based augmentation and a hybrid CNN-LSTM model for improving speaker-independent keyword recognition on limited datasets
Keyword recognition is the basis of speech recognition, and its application is rapidly
increasing in keyword spotting, robotics, and smart home surveillance. Because of these …
increasing in keyword spotting, robotics, and smart home surveillance. Because of these …
Iteratively Improving Speech Recognition and Voice Conversion
MK Singh, N Takahashi, O Naoyuki - arXiv preprint arXiv:2305.15055, 2023 - arxiv.org
Many existing works on voice conversion (VC) tasks use automatic speech recognition
(ASR) models for ensuring linguistic consistency between source and converted samples …
(ASR) models for ensuring linguistic consistency between source and converted samples …
Comparison of modern and traditional Slovak children's speech recognition
We compare two distinct speech recognition approaches, namely Hidden Markov models
mixed with deep neural networks and modern end-to-end neural speech recognition …
mixed with deep neural networks and modern end-to-end neural speech recognition …
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion
We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice
conversion applied to data augmentation for automatic speech recognition (ASR) systems in …
conversion applied to data augmentation for automatic speech recognition (ASR) systems in …
Enhancing Automatic Speech Recognition with Personalized Models: Improving Accuracy through Individualized Fine-tuning
V Brydinskyi, D Sabodashko, Y Khoma… - IEEE …, 2024 - ieeexplore.ieee.org
Automatic speech recognition (ASR) systems have become increasingly popular in recent
years due to their ability to convert spoken language into text. Nonetheless, despite their …
years due to their ability to convert spoken language into text. Nonetheless, despite their …
Non-Parallel Voice Conversion for ASR Augmentation
Automatic speech recognition (ASR) needs to be robust to speaker differences. Voice
Conversion (VC) modifies speaker characteristics of input speech. This is an attractive …
Conversion (VC) modifies speaker characteristics of input speech. This is an attractive …
Improving child speech recognition with augmented child-like speech
State-of-the-art ASRs show suboptimal performance for child speech. The scarcity of child
speech limits the development of child speech recognition (CSR). Therefore, we studied …
speech limits the development of child speech recognition (CSR). Therefore, we studied …