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20162026
most citedA Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition

173 citations · 306 across the 58 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

eess.AS2024

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…

cs.IT2024

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…

cs.CL2024

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…

cs.SD2024

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…

cs.CL2024★ 3 cited

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…

eess.AS2024

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…