8 citations · 17 across the 16 of their papers we have counts for
16 papers
Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings
Ryo Fukuda, Takatomo Kano, Siddhant Arora +7
We investigate turn-taking in multimodal multi-party conversations using large language models (LLMs). We construct an evaluation framework for three tasks: addressee detection, tu…
Microphone Array Geometry Independent Multi-Talker Distant ASR: NTT System for the DASR Task of the CHiME-8 Challenge
Naoyuki Kamo, Naohiro Tawara, Atsushi Ando +15
In this paper, we introduce a multi-talker distant automatic speech recognition (DASR) system we designed for the DASR task 1 of the CHiME-8 challenge. Our system performs speaker…
NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge
Naoyuki Kamo, Naohiro Tawara, Atsushi Ando +15
We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track. It consists of a diarization first pipeline. For diarization, we use end-to-en…
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
This paper introduces a novel approach called sentence-wise speech summarization (Sen-SSum), which generates text summaries from a spoken document in a sentence-by-sentence manner.…
Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over
Atsunori Ogawa, Naoyuki Kamo, Kohei Matsuura +5
Large language models (LLMs) have been successfully applied for rescoring automatic speech recognition (ASR) hypotheses. However, their ability to rescore ASR hypotheses of casual…
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition
Atsunori Ogawa, Naohiro Tawara, Takatomo Kano +1
Confidence estimation, in which we estimate the reliability of each recognized token (e.g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and dete…
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models
Atsunori Ogawa, Naohiro Tawara, Marc Delcroix +1
We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses. Previou…
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition
Atsunori Ogawa, Takafumi Moriya, Naoyuki Kamo +2
We propose a new shallow fusion (SF) method to exploit an external backward language model (BLM) for end-to-end automatic speech recognition (ASR). The BLM has complementary charac…
NTT speaker diarization system for CHiME-7: multi-domain, multi-microphone End-to-end and vector clustering diarization
Naohiro Tawara, Marc Delcroix, Atsushi Ando +1
This paper details our speaker diarization system designed for multi-domain, multi-microphone casual conversations. The proposed diarization pipeline uses weighted prediction error…
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model. This approach is promising because it, in contras…
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…
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Marc Delcroix, Naohiro Tawara, Mireia Diez +6
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC esti…
Leveraging Large Text Corpora for End-to-End Speech Summarization
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech re…
Effective data screening technique for crowdsourced speech intelligibility experiments: Evaluation with IRM-based speech enhancement
Ayako Yamamoto, Toshio Irino, Shoko Araki +4
It is essential to perform speech intelligibility (SI) experiments with human listeners in order to evaluate objective intelligibility measures for developing effective speech enha…
Attention-based Multi-hypothesis Fusion for Speech Summarization
Takatomo Kano, Atsunori Ogawa, Marc Delcroix +1
Speech summarization, which generates a text summary from speech, can be achieved by combining automatic speech recognition (ASR) and text summarization (TS). With this cascade app…
Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility
Ayako Yamamoto, Toshio Irino, Kenichi Arai +4
Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct exper…