173 citations · 306 across the 58 of their papers we have counts for
12 papers · 1 filter
On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition
Nick Rossenbach, Benedikt Hilmes, Ralf Schlüter
Synthetic data generated by text-to-speech (TTS) systems can be used to improve automatic speech recognition (ASR) systems in low-resource or domain mismatch tasks. It has been sho…
Investigating the Effect of Language Models in Sequence Discriminative Training for Neural Transducers
Zijian Yang, Wei Zhou, Ralf Schlüter +1
In this work, we investigate the effect of language models (LMs) with different context lengths and label units (phoneme vs. word) used in sequence discriminative training for phon…
End-to-End Training of a Neural HMM with Label and Transition Probabilities
Daniel Mann, Tina Raissi, Wilfried Michel +2
We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modele…
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.…
Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription
Peter Vieting, Simon Berger, Thilo von Neumann +3
Many real-life applications of automatic speech recognition (ASR) require processing of overlapped speech. A common method involves first separating the speech into overlap-free st…
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition
Mohammad Zeineldeen, Albert Zeyer, Ralf Schlüter +1
We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks.…
Comparative Analysis of the wav2vec 2.0 Feature Extractor
Peter Vieting, Ralf Schlüter, Hermann Ney
Automatic speech recognition (ASR) systems typically use handcrafted feature extraction pipelines. To avoid their inherent information loss and to achieve more consistent modeling…
Mixture Encoder for Joint Speech Separation and Recognition
Simon Berger, Peter Vieting, Christoph Boeddeker +2
Multi-speaker automatic speech recognition (ASR) is crucial for many real-world applications, but it requires dedicated modeling techniques. Existing approaches can be divided into…
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…
RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition
Wei Zhou, Eugen Beck, Simon Berger +2
Modern public ASR tools usually provide rich support for training various sequence-to-sequence (S2S) models, but rather simple support for decoding open-vocabulary scenarios only.…
End-to-End Speech Recognition: A Survey
Rohit Prabhavalkar, Takaaki Hori, Tara N. Sainath +2
In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, c…
Analyzing And Improving Neural Speaker Embeddings for ASR
Christoph Lüscher, Jingjing Xu, Mohammad Zeineldeen +2
Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated t…