8 citations · 12 across the 15 of their papers we have counts for
15 papers
Refining Pseudo-Audio Prompts with Speech-Text Alignment for Text-Only Domain Adaptation in LLM-Based ASR
Ryo Magoshi, Takashi Maekaku, Yusuke Shinohara
LLM-based automatic speech recognition models demonstrate strong performance by connecting audio encoders and LLMs. However, data scarcity of paired speech and transcription often…
Bagpiper-TTS: Natural Language Guided Universal Speech Synthesis
Jinchuan Tian, Haoran Wang, Siddhant Arora +6
Classical TTS systems typically rely on rigid input formats and predefined metadata slots, limiting their ability to fulfill flexible user requirements. This paper introduces Bagpi…
Online Predictive Coding for Dual-Mode Self-Supervised Speech Model
Keita Goto, Takashi Maekaku, Jin Sakuma +3
Dual-mode self-supervised speech models are pre-trained to handle streaming and non-streaming conditions simultaneously. However, their attention is computed over different context…
Online Register for Dual-Mode Self-Supervised Speech Models: Mitigating The Lack of Future Context
Keita Goto, Takashi Maekaku, Jin Sakuma +3
Dual-mode self-supervised speech models (S3Ms), which jointly pre-trained in the offline and online mode, suffer from attention mismatch in streaming scenarios due to missing futur…
Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions
Jinchuan Tian, Haoran Wang, Bo-Hao Su +14
Current audio foundation models typically rely on rigid, task-specific supervision (e.g., speech recognition), addressing isolated factors of audio rather than the whole. In contra…
Evaluating Self-Supervised Speech Models via Text-Based LLMS
Takashi Maekaku, Keita Goto, Jinchuan Tian +2
Self-Supervised Learning (SSL) has gained traction for its ability to learn rich representations with low labeling costs, applicable across diverse downstream tasks. However, asses…
OpusLM: A Family of Open Unified Speech Language Models
Jinchuan Tian, William Chen, Yifan Peng +9
This paper presents Open Unified Speech Language Models (OpusLMs), a family of open foundational speech language models (SpeechLMs) up to 7B. Initialized from decoder-only text lan…
ESPnet-SpeechLM: An Open Speech Language Model Toolkit
Jinchuan Tian, Jiatong Shi, William Chen +13
We present ESPnet-SpeechLM, an open toolkit designed to democratize the development of speech language models (SpeechLMs) and voice-driven agentic applications. The toolkit standar…
LV-CTC: Non-autoregressive ASR with CTC and latent variable models
Yuya Fujita, Shinji Watanabe, Xuankai Chang +1
Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of…
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model
Takashi Maekaku, Jiatong Shi, Xuankai Chang +2
Recently, the usefulness of self-supervised representation learning (SSRL) methods has been confirmed in various downstream tasks. Many of these models, as exemplified by HuBERT an…
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Xuankai Chang, Brian Yan, Kwanghee Choi +14
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features…
Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learning
Xuankai Chang, Brian Yan, Yuya Fujita +2
Self-supervised learning (SSL) of speech has shown impressive results in speech-related tasks, particularly in automatic speech recognition (ASR). While most methods employ the out…
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation
Xuankai Chang, Takashi Maekaku, Yuya Fujita +1
This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech…
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition
Xuankai Chang, Takashi Maekaku, Pengcheng Guo +8
Self-supervised pretraining on speech data has achieved a lot of progress. High-fidelity representation of the speech signal is learned from a lot of untranscribed data and shows p…
Speech Representation Learning Combining Conformer CPC with Deep Cluster for the ZeroSpeech Challenge 2021
Takashi Maekaku, Xuankai Chang, Yuya Fujita +3
We present a system for the Zero Resource Speech Challenge 2021, which combines a Contrastive Predictive Coding (CPC) with deep cluster. In deep cluster, we first prepare pseudo-la…