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Found 6 papers, 4 papers with code
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions
We perform experiments using 10 minutes of transcribed speech from English (for replicating prior work) and two additional pairs of languages differing in the availability of supplemental text data: Gronings and Frisian (~7. 5M token corpora available), and Besemah and Nasal (only small lexica available).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Quantifying Language Variation Acoustically with Few Resources
Deep acoustic models represent linguistic information based on massive amounts of data.
Automated speech tools for helping communities process restricted-access corpora for language revival efforts
An even narrower bottleneck occurs for recordings with access constraints, such as language that must be vetted or filtered by authorised community members before annotation can begin.
Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
For many (minority) languages, the resources needed to train large models are not available.
Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages
Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural Representations for Modeling Variation in Speech
We show that speech representations extracted from a specific type of neural model (i. e. Transformers) lead to a better match with human perception than two earlier approaches on the basis of phonetic transcriptions and MFCC-based acoustic features.
