Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

We present a simple and efficient auxiliary loss function for automatic speech recognition
(ASR) based on the connectionist temporal classification (CTC) objective. The proposed …

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the
human brain and have been widely applied in speech processing. The application areas of …

There is an implicit assumption that traditional hybrid approaches for automatic speech
recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to …

Recently, end-to-end automatic speech recognition models based on connectionist temporal
classification (CTC) have achieved impressive results, especially when fine-tuned from …

The end-to-end (E2E) model allows for training of automatic speech recognition (ASR)
systems without having to consider the acoustic model, lexicon, language model and …

This article gives an overview of our symbiotic human-robot interaction project, which aims
at an autonomous android who behaves and interacts just like a human. A conversational …

Encoder-decoder models for acoustic-to-word (A2W) automatic speech recognition (ASR)
are attractive for their simplicity of architecture and run-time latency while achieving state-of …

This paper presents an audio visual automatic speech recognition (AV-ASR) system using a
Transformer-based architecture. We particularly focus on the scene context provided by the …

The network architecture of end-to-end (E2E) automatic speech recognition (ASR) can be
classified into several models, including connectionist temporal classification (CTC) …