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Adapting Language Balance in Code-Switching Speech
Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel
Despite achieving impressive results on standard benchmarks, large foundational models still struggle against code-switching test cases. When data scarcity cannot be used as the us…
Bayesian Low-Rank Factorization for Robust Model Adaptation
Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel
Large speech foundation models achieve strong performance across many domains, but they often require adaptation to handle local needs such as code-switching, where speakers mix la…
A Cocktail-Party Benchmark: Multi-Modal dataset and Comparative Evaluation Results
Thai-Binh Nguyen, Katerina Zmolikova, Pingchuan Ma +3
We introduce the task of Multi-Modal Context-Aware Recognition (MCoRec) in the ninth CHiME Challenge, which addresses the cocktail-party problem of overlapping conversations in a s…
Streaming Non-Autoregressive Model for Accent Conversion and Pronunciation Improvement
Tuan-Nam Nguyen, Ngoc-Quan Pham, Seymanur Akti +1
We propose a first streaming accent conversion (AC) model that transforms non-native speech into a native-like accent while preserving speaker identity, prosody and improving pronu…
Weight Factorization and Centralization for Continual Learning in Speech Recognition
Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel
Modern neural network based speech recognition models are required to continually absorb new data without re-training the whole system, especially in downstream applications using…
PIER: A Novel Metric for Evaluating What Matters in Code-Switching
Enes Yavuz Ugan, Ngoc-Quan Pham, Leonard Bärmann +1
Code-switching, the alternation of languages within a single discourse, presents a significant challenge for Automatic Speech Recognition. Despite the unique nature of the task, pe…