9 citations · 24 across the 33 of their papers we have counts for
34 papers
HINTT Submission to the 2nd MLC-SLM Challenge: Comparing Cascaded and Unified Approaches to Diarization and ASR
Takanori Ashihara, Kohei Matsuura, Masato Mimura
This paper presents the HINTT system submitted to the 2nd Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM). We address multilingual speaker-att…
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…
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…
Frontend Token Enhancement for Token-Based Speech Recognition
Takanori Ashihara, Shota Horiguchi, Kohei Matsuura +2
Discretized representations of speech signals are efficient alternatives to continuous features for various speech applications, including automatic speech recognition (ASR) and sp…
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…
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…
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…
TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models
Junyi Peng, Takanori Ashihara, Marc Delcroix +4
Self-supervised learning (SSL) models have significantly advanced speech processing tasks, and several benchmarks have been proposed to validate their effectiveness. However, previ…
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…
Investigation of Speaker Representation for Target-Speaker Speech Processing
Takanori Ashihara, Takafumi Moriya, Shota Horiguchi +5
Target-speaker speech processing (TS) tasks, such as target-speaker automatic speech recognition (TS-ASR), target speech extraction (TSE), and personal voice activity detection (p-…
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…
Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding
Takafumi Moriya, Takanori Ashihara, Masato Mimura +4
A hybrid autoregressive transducer (HAT) is a variant of neural transducer that models blank and non-blank posterior distributions separately. In this paper, we propose a novel int…
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…
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…
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.…
SpeakerBeam-SS: Real-time Target Speaker Extraction with Lightweight Conv-TasNet and State Space Modeling
Hiroshi Sato, Takafumi Moriya, Masato Mimura +6
Real-time target speaker extraction (TSE) is intended to extract the desired speaker's voice from the observed mixture of multiple speakers in a streaming manner. Implementing real…
Lightweight Zero-shot Text-to-Speech with Mixture of Adapters
Kenichi Fujita, Takanori Ashihara, Marc Delcroix +1
The advancements in zero-shot text-to-speech (TTS) methods, based on large-scale models, have demonstrated high fidelity in reproducing speaker characteristics. However, these mode…
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…
Probing Self-supervised Learning Models with Target Speech Extraction
Junyi Peng, Marc Delcroix, Tsubasa Ochiai +4
Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-…
What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis
Takanori Ashihara, Marc Delcroix, Takafumi Moriya +3
Self-supervised learning (SSL) has attracted increased attention for learning meaningful speech representations. Speech SSL models, such as WavLM, employ masked prediction training…
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters
Kenichi Fujita, Hiroshi Sato, Takanori Ashihara +4
The zero-shot text-to-speech (TTS) method, based on speaker embeddings extracted from reference speech using self-supervised learning (SSL) speech representations, can reproduce sp…
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…
SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
Takanori Ashihara, Takafumi Moriya, Kohei Matsuura +5
Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition. More recently, speech SS…