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Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation
Sara Papi, Peidong Wang, Junkun Chen +4
The growing need for instant spoken language transcription and translation is driven by increased global communication and cross-lingual interactions. This has made offering transl…
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
Ziyi Yang, Mahmoud Khademi, Yichong Xu +16
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Sanyuan Chen, Chengyi Wang, Zhengyang Chen +15
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contai…
Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition
Zhong Meng, Yashesh Gaur, Naoyuki Kanda +4
Text-only adaptation of an end-to-end (E2E) model remains a challenging task for automatic speech recognition (ASR). Language model (LM) fusion-based approaches require an addition…
Speech-language Pre-training for End-to-end Spoken Language Understanding
Yao Qian, Ximo Bian, Yu Shi +4
End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from speech signal without cascading an automatic speech recognizer (ASR) with a natural language…
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer
Liang Lu, Zhong Meng, Naoyuki Kanda +2
Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end acoustic model that extends the standard Recurrent Neural Network Transducer (RNN-T) for the purpose of the…
Serialized Output Training for End-to-End Overlapped Speech Recognition
Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang +2
This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instea…
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models
Naoyuki Kanda, Shota Horiguchi, Yusuke Fujita +3
This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recor…
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition
Naoyuki Kanda, Shota Horiguchi, Ryoichi Takashima +3
In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR). Our method automatically extracts and transcribes target speaker's…
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
Naoyuki Kanda, Christoph Boeddeker, Jens Heitkaemper +4
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for…