7 papers · 1 filter
Unified Learnable 2D Convolutional Feature Extraction for ASR
Peter Vieting, Benedikt Hilmes, Ralf Schlüter +1
Neural front-ends represent a promising approach to feature extraction for automatic speech recognition (ASR) systems as they enable to learn specifically tailored features for dif…
A Comparative Analysis on ASR System Combination for Attention, CTC, Factored Hybrid, and Transducer Models
Noureldin Bayoumi, Robin Schmitt, Tina Raissi +3
Combination approaches for speech recognition (ASR) systems cover structured sentence-level or word-based merging techniques as well as combination of model scores during beam sear…
Dynamic Acoustic Model Architecture Optimization in Training for ASR
Jingjing Xu, Zijian Yang, Albert Zeyer +3
Architecture design is inherently complex. Existing approaches rely on either handcrafted rules, which demand extensive empirical expertise, or automated methods like neural archit…
Label-Context-Dependent Internal Language Model Estimation for CTC
Zijian Yang, Minh-Nghia Phan, Ralf Schlüter +1
Although connectionist temporal classification (CTC) has the label context independence assumption, it can still implicitly learn a context-dependent internal language model (ILM)…
Regularizing Learnable Feature Extraction for Automatic Speech Recognition
Peter Vieting, Maximilian Kannen, Benedikt Hilmes +2
Neural front-ends are an appealing alternative to traditional, fixed feature extraction pipelines for automatic speech recognition (ASR) systems since they can be directly trained…
Classification Error Bound for Low Bayes Error Conditions in Machine Learning
Zijian Yang, Vahe Eminyan, Ralf Schlüter +1
In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknow…
Right Label Context in End-to-End Training of Time-Synchronous ASR Models
Tina Raissi, Ralf Schlüter, Hermann Ney
Current time-synchronous sequence-to-sequence automatic speech recognition (ASR) models are trained by using sequence level cross-entropy that sums over all alignments. Due to the…