173 citations · 306 across the 58 of their papers we have counts for
63 papers
A Systematic Comparison of Search Space Complexity and Efficiency for Time-Synchronous End-to-End ASR Models
Simon Berger, Larissa Kleppel, Ralf Schlüter
Search is a central component of Automatic Speech Recognition (ASR) systems and has been deeply investigated for classical ASR modeling based on Hidden Markov Models (HMMs). Howeve…
Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs
Albert Zeyer, Ralf Schlüter, Hermann Ney
Speech-to-text alignment means finding the temporal boundaries of each word in the audio. Some models provide such an alignment directly and others do not. Connectionist temporal c…
Revisiting the Relation Between Language Model Perplexity and ASR Word Error Rate for Modern End-to-End Speech Recognition
Mohammad Zeineldeen, Albert Zeyer, Haoran Zhang +3
Language model (LM) perplexity (PPL) has historically been used as a proxy for automatic speech recognition (ASR) word error rate (WER), with prior work reporting an approximately…
Text-Utilization for Encoder-dominated Speech Recognition Models
Albert Zeyer, Tim Posielek, Ralf Schlüter +1
This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facilitate faster recognition. We pr…
Diffusion Language Models for Speech Recognition
Davyd Naveriani, Albert Zeyer, Ralf Schlüter +1
Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention and parallel text generation.…
LLMs and Speech: Integration vs. Combination
Robin Schmitt, Albert Zeyer, Mohammad Zeineldeen +2
In this work, we study different approaches to utilize large language models (LLMs) for automatic speech recognition (ASR). Specifically, we compare the tight integration of an aco…
Reproducing and Dissecting Denoising Language Models for Speech Recognition
Dorian Koch, Albert Zeyer, Nick Rossenbach +2
Denoising language models (DLMs) have been proposed as a powerful alternative to traditional language models (LMs) for automatic speech recognition (ASR), motivated by their abilit…
Error Analysis in a Modular Meeting Transcription System
Peter Vieting, Simon Berger, Thilo von Neumann +3
Meeting transcription is a field of high relevance and remarkable progress in recent years. Still, challenges remain that limit its performance. In this work, we extend a previousl…
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…
Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language
Kristin Gnadt, David Thulke, Simone Kopeinik +1
In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods…
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…
Analyzing the Importance of Blank for CTC-Based Knowledge Distillation
Benedikt Hilmes, Nick Rossenbach, Ralf Schlüter
With the rise of large pre-trained foundation models for automatic speech recognition new challenges appear. While the performance of these models is good, runtime and cost of infe…
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…
The Conformer Encoder May Reverse the Time Dimension
Robin Schmitt, Albert Zeyer, Mohammad Zeineldeen +2
We sometimes observe monotonically decreasing cross-attention weights in our Conformer-based global attention-based encoder-decoder (AED) models, Further investigation shows that t…
Refined Statistical Bounds for Classification Error Mismatches with Constrained Bayes Error
Zijian Yang, Vahe Eminyan, Ralf Schlüter +1
In statistical classification/multiple hypothesis testing and machine learning, a model distribution estimated from the training data is usually applied to replace the unknown true…
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition
Nick Rossenbach, Ralf Schlüter, Sakriani Sakti
The rapid development of neural text-to-speech (TTS) systems enabled its usage in other areas of natural language processing such as automatic speech recognition (ASR) or spoken la…
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 Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures
Benedikt Hilmes, Nick Rossenbach, and Ralf Schlüter
In this work we evaluate the utility of synthetic data for training automatic speech recognition (ASR). We use the ASR training data to train a text-to-speech (TTS) system similar…
Medical Spoken Named Entity Recognition
Khai Le-Duc, David Thulke, Hung-Phong Tran +4
Spoken Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc. In this work, we present V…
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.…