29 citations · 150 across the 31 of their papers we have counts for
5 papers · 1 filter
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.…
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…
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech
Tobias Menne, Ilya Sklyar, Ralf Schlüter +1
Significant performance degradation of automatic speech recognition (ASR) systems is observed when the audio signal contains cross-talk. One of the recently proposed approaches to…