173 citations · 306 across the 58 of their papers we have counts for
8 papers · 1 filter
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…
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)…
Investigating the Effect of Label Topology and Training Criterion on ASR Performance and Alignment Quality
Tina Raissi, Christoph Lüscher, Simon Berger +2
The ongoing research scenario for automatic speech recognition (ASR) envisions a clear division between end-to-end approaches and classic modular systems. Even though a high-level…
On the Relation between Internal Language Model and Sequence Discriminative Training for Neural Transducers
Zijian Yang, Wei Zhou, Ralf Schlüter +1
Internal language model (ILM) subtraction has been widely applied to improve the performance of the RNN-Transducer with external language model (LM) fusion for speech recognition.…
Competitive and Resource Efficient Factored Hybrid HMM Systems are Simpler Than You Think
Tina Raissi, Christoph Lüscher, Moritz Gunz +2
Building competitive hybrid hidden Markov model~(HMM) systems for automatic speech recognition~(ASR) requires a complex multi-stage pipeline consisting of several training criteria…
HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch
Tina Raissi, Wei Zhou, Simon Berger +2
In this work, we compare from-scratch sequence-level cross-entropy (full-sum) training of Hidden Markov Model (HMM) and Connectionist Temporal Classification (CTC) topologies for a…
Improving Factored Hybrid HMM Acoustic Modeling without State Tying
Tina Raissi, Eugen Beck, Ralf Schlüter +1
In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored…
Towards Consistent Hybrid HMM Acoustic Modeling
Tina Raissi, Eugen Beck, Ralf Schlüter +1
High-performance hybrid automatic speech recognition (ASR) systems are often trained with clustered triphone outputs, and thus require a complex training pipeline to generate the c…