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How can we learn representations of phonological features that are useful for speech recognition? 


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To learn representations of phonological features that are useful for speech recognition, several approaches have been proposed in the literature. One approach is to exploit phonological knowledge and linguistic expertise by incorporating phonological features into the speech emotion recognition (SER) task . Another approach is to use unsupervised speech representation learning, which has shown success in finding representations that correlate with phonetic structures and improve downstream speech recognition performance . Additionally, pre-trained acoustic representations have been found to acquire phonetic properties and encode transferable features of speech, with simpler architectures often performing better . Furthermore, using self-supervised speech representations, such as Wav2Vec and XLSR, has been shown to improve word error rate performance in speech recognition for impaired speech . These approaches highlight the importance of leveraging phonological features and pre-trained representations for effective speech recognition.

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The study suggests that using Wav2Vec self-supervised speech representations pretrained on large unlabelled data can improve word error rate (WER) performance in dysarthric speech recognition.
Open accessProceedings ArticleDOI
Danni Ma, Neville Ryant, Mark Liberman 
06 Jun 2021
11 Citations
Pre-trained acoustic representations like wav2vec and DeCoAR can learn transferable features for speech recognition, with DeCoAR performing the best among the tested systems.
Open accessProceedings ArticleDOI
29 Jan 2020
62 Citations
We can learn representations of phonological features that are useful for speech recognition by training on diverse and noisy speech data, and evaluating their robustness and transferability to other languages.
The paper proposes a novel approach to learn representations of phonological features and fuse them with acoustic features for improved speech emotion recognition.
The paper proposes a shared encoder-multi decoder model for learning phonological knowledge representations, which are useful for speech recognition.

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