4 citations · 18 across the 28 of their papers we have counts for
8 papers · 1 filter
All-in-One ASR: Unifying Encoder-Decoder Models of CTC, Attention, and Transducer in Dual-Mode ASR
Takafumi Moriya, Masato Mimura, Tomohiro Tanaka +3
This paper proposes a unified framework, All-in-One ASR, that allows a single model to support multiple automatic speech recognition (ASR) paradigms, including connectionist tempor…
Generic Speech Enhancement with Self-Supervised Representation Space Loss
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +3
Single-channel speech enhancement is utilized in various tasks to mitigate the effect of interfering signals. Conventionally, to ensure the speech enhancement performs optimally, t…
Alignment-Free Training for Transducer-based Multi-Talker ASR
Takafumi Moriya, Shota Horiguchi, Marc Delcroix +5
Extending the RNN Transducer (RNNT) to recognize multi-talker speech is essential for wider automatic speech recognition (ASR) applications. Multi-talker RNNT (MT-RNNT) aims to ach…
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai +7
Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for…
Improving Scheduled Sampling for Neural Transducer-based ASR
Takafumi Moriya, Takanori Ashihara, Hiroshi Sato +3
The recurrent neural network-transducer (RNNT) is a promising approach for automatic speech recognition (ASR) with the introduction of a prediction network that autoregressively co…
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss
Hiroshi Sato, Ryo Masumura, Tsubasa Ochiai +8
Self-supervised learning (SSL) is the latest breakthrough in speech processing, especially for label-scarce downstream tasks by leveraging massive unlabeled audio data. The noise r…
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +6
Target speech extraction is a technique to extract the target speaker's voice from mixture signals using a pre-recorded enrollment utterance that characterize the voice characteris…
A Transformer-based Audio Captioning Model with Keyword Estimation
Yuma Koizumi, Ryo Masumura, Kyosuke Nishida +2
One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be descr…