27 citations · 50 across the 8 of their papers we have counts for
9 papers
Training-Free Contextual ASR via SpeechLLM-Based Error-Aware Selective Retrieval
Natsuo Yamashita, Ai Nemoto, Ryosuke Koichi +1
Recognition of domain-specific and low-frequency terms remains challenging for automatic speech recognition (ASR). Although contextual biasing can improve their recognition, direct…
Model-free Speculative Decoding for Transformer-based ASR with Token Map Drafting
Tuan Vu Ho, Hiroaki Kokubo, Masaaki Yamamoto +1
End-to-end automatic speech recognition (ASR) systems based on transformer architectures, such as Whisper, offer high transcription accuracy and robustness. However, their autoregr…
LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context
Natsuo Yamashita, Masaaki Yamamoto, Hiroaki Kokubo +1
Generative error correction (GER) with large language models (LLMs) has emerged as an effective post-processing approach to improve automatic speech recognition (ASR) performance.…
End-to-End Integration of Speech Emotion Recognition with Voice Activity Detection using Self-Supervised Learning Features
Natsuo Yamashita, Masaaki Yamamoto, Yohei Kawaguchi
Speech Emotion Recognition (SER) often operates on speech segments detected by a Voice Activity Detection (VAD) model. However, VAD models may output flawed speech segments, especi…
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection
Harsh Purohit, Takashi Endo, Masaaki Yamamoto +1
This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), vari…
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
Kota Dohi, Keisuke Imoto, Noboru Harada +7
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring ap…
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task
Kota Dohi, Tomoya Nishida, Harsh Purohit +5
We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). Domain shifts are differences in data distributions that can d…
Anomalous Sound Detection Based on Machine Activity Detection
Tomoya Nishida, Kota Dohi, Takashi Endo +2
We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active.…
Environmental Sound Extraction Using Onomatopoeic Words
Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto +2
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We…