12 citations · 27 across the 29 of their papers we have counts for
32 papers
How Reliable Are Predicted MOS for Reproducing Human System-Level Preferences in Speech Enhancement?
Nahomi Kusunoki, Tsubasa Ochiai, Naohiro Tawara +4
We investigate whether predicted Mean Opinion Scores (MOS) can reliably support system-level comparisons of speech enhancement (SE) methods by introducing system-level preference a…
Improving Predicted MOS Scores, Not Perceived Quality: Multi-Predictor Test-Time Optimization of Enhanced Speech
Tsubasa Ochiai, Marc Delcroix, Nahomi Kusunoki +5
Non-intrusive MOS predictors are widely used instead of subjective listening tests to evaluate and rank speech enhancement (SE) systems. If they accurately reflect perceived qualit…
Diarization Error Decomposition Under Pause Annotation Ambiguity
Shota Horiguchi, Marc Delcroix, Naohiro Tawara +1
Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morpholog…
Over-Tightening-Aware Pseudo-Labeling for Tight-Boundary Speaker Diarization
Shota Horiguchi, Takanori Ashihara, Marc Delcroix +2
Training speaker diarization models on loose labels, such as speech segments with padded boundaries or filled pauses, often results in similarly loose model outputs. To obtain tigh…
Ontology-based Target Sound Extraction
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +2
Target sound extraction (TSE) aims to isolate a sound source of interest from a mixture, given a semantic query. Existing TSE systems are conditioned on fixed class representations…
M3-DuplexBench: A Multi-Turn, Multilingual, Multidomain Benchmark for Full-Duplex Spoken Dialogue Models
Ryo Fukuda, Atsushi Ando, Hiroki Kanagawa +4
Full-duplex spoken dialogue systems (FDSDSs) can listen while speaking, enabling natural conversational behaviors such as smooth turn-taking, backchannel handling, and user barge-i…
SphereVBx: Spherical Variational Bayes Clustering for Simplified EEND-VC Diarization
Petr Pálka, Jiangyu Han, Prachi Singh +3
We propose SphereVBx, a Bayesian clustering framework for hyperspherical embeddings based on Toroidal Probabilistic Spherical Discriminant Analysis (T-PSDA). The method follows the…
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…
Tight Boundary Prediction in Speaker Diarization Using Causal-Anticausal Consistency
Shota Horiguchi, Marc Delcroix, Naohiro Tawara +2
Multi-talker conversational automatic speech recognition data are often used to train speaker diarization models. Because such data prioritize semantic continuity, pauses and bound…
Who Spoke What When? Evaluating Spoken Language Models for Conversational ASR with Semantic and Overlap-Aware Metrics
Naohiro Tawara, Samuele Cornell, Alexander Polok +3
Conversational automatic speech recognition remains challenging due to overlapping speech, far-field noise, and varying speaker counts. While recent LLM-based systems perform well…
VBx for End-to-End Neural and Clustering-based Diarization
Petr Pálka, Jiangyu Han, Marc Delcroix +2
We present improvements to speaker diarization in the two-stage end-to-end neural diarization with vector clustering (EEND-VC) framework. The first stage employs a Conformer-based…
Can We Really Repurpose Multi-Speaker ASR Corpus for Speaker Diarization?
Shota Horiguchi, Naohiro Tawara, Takanori Ashihara +2
Neural speaker diarization is widely used for overlap-aware speaker diarization, but it requires large multi-speaker datasets for training. To meet this data requirement, large dat…
Mitigating Non-Target Speaker Bias in Guided Speaker Embedding
Shota Horiguchi, Takanori Ashihara, Marc Delcroix +2
Obtaining high-quality speaker embeddings in multi-speaker conditions is crucial for many applications. A recently proposed guided speaker embedding framework, which utilizes speec…
Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
Alexis Plaquet, Naohiro Tawara, Marc Delcroix +4
End-to-End Neural Diarization with Vector Clustering is a powerful and practical approach to perform Speaker Diarization. Multiple enhancements have been proposed for the segmentat…
Pretraining Multi-Speaker Identification for Neural Speaker Diarization
Shota Horiguchi, Atsushi Ando, Marc Delcroix +1
End-to-end speaker diarization enables accurate overlap-aware diarization by jointly estimating multiple speakers' speech activities in parallel. This approach is data-hungry, requ…
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…
Mamba-based Segmentation Model for Speaker Diarization
Alexis Plaquet, Naohiro Tawara, Marc Delcroix +3
Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization,…
Guided Speaker Embedding
Shota Horiguchi, Takafumi Moriya, Atsushi Ando +4
This paper proposes a guided speaker embedding extraction system, which extracts speaker embeddings of the target speaker using speech activities of target and interference speaker…
SoundBeam meets M2D: Target Sound Extraction with Audio Foundation Model
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +4
Target sound extraction (TSE) consists of isolating a desired sound from a mixture of arbitrary sounds using clues to identify it. A TSE system requires solving two problems at onc…
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…
Recursive Attentive Pooling for Extracting Speaker Embeddings from Multi-Speaker Recordings
Shota Horiguchi, Atsushi Ando, Takafumi Moriya +4
This paper proposes a method for extracting speaker embedding for each speaker from a variable-length recording containing multiple speakers. Speaker embeddings are crucial not onl…
Interaural time difference loss for binaural target sound extraction
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +3
Binaural target sound extraction (TSE) aims to extract a desired sound from a binaural mixture of arbitrary sounds while preserving the spatial cues of the desired sound. Indeed, f…
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…