31 citations · 37 across the 33 of their papers we have counts for
34 papers
Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning
Hayato Futami, Hassan Shahmohammadi, Tushar Dhyani +5
Speech-to-speech translation (S2ST) has advanced significantly with speech LLMs, offering the potential for joint optimization and preserving non-linguistic information. However, t…
Phonological Tokenizer: Prosody-Aware Phonetic Token via Multi-Objective Fine-Tuning with Differentiable K-Means
Kentaro Onda, Hayato Futami, Yosuke Kashiwagi +2
In recent years, there has been growing interest in representing speech with discrete tokens, which serve as pseudo-text for speech language models (speechLMs) and as efficient int…
Optimizing Conversational Quality in Spoken Dialogue Systems with Reinforcement Learning from AI Feedback
Siddhant Arora, Jinchuan Tian, Jiatong Shi +4
Reinforcement learning from human or AI feedback (RLHF/RLAIF) for speech-in/speech-out dialogue systems (SDS) remains underexplored, with prior work largely limited to single seman…
Chain-of-Thought Reasoning in Streaming Full-Duplex End-to-End Spoken Dialogue Systems
Siddhant Arora, Jinchuan Tian, Hayato Futami +4
Most end-to-end (E2E) spoken dialogue systems (SDS) rely on voice activity detection (VAD) for turn-taking, but VAD fails to distinguish between pauses and turn completions. Duplex…
Spiralformer: Low Latency Encoder for Streaming Speech Recognition with Circular Layer Skipping and Early Exiting
Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi +2
For streaming speech recognition, a Transformer-based encoder has been widely used with block processing. Although many studies addressed improving emission latency of transducers,…
Scheduled Interleaved Speech-Text Training for Speech-to-Speech Translation with LLMs
Hayato Futami, Emiru Tsunoo, Yosuke Kashiwagi +4
Speech-to-speech translation (S2ST) has been advanced with large language models (LLMs), which are fine-tuned on discrete speech units. In such approaches, modality adaptation from…
Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data
Yosuke Kashiwagi, Hayato Futami, Emiru Tsunoo +1
This paper reports on the development of a large-scale speech recognition model, Whale. Similar to models such as Whisper and OWSM, Whale leverages both a large model size and a di…
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems
Siddhant Arora, Jinchuan Tian, Hayato Futami +5
Unlike traditional cascaded pipelines, end-to-end (E2E) spoken dialogue systems preserve full differentiability and capture non-phonemic information, making them well-suited for mo…
Differentiable K-means for Fully-optimized Discrete Token-based ASR
Kentaro Onda, Yosuke Kashiwagi, Emiru Tsunoo +2
Recent studies have highlighted the potential of discrete tokens derived from self-supervised learning (SSL) models for various speech-related tasks. These tokens serve not only as…
ESPnet-SDS: Unified Toolkit and Demo for Spoken Dialogue Systems
Siddhant Arora, Yifan Peng, Jiatong Shi +9
Advancements in audio foundation models (FMs) have fueled interest in end-to-end (E2E) spoken dialogue systems, but different web interfaces for each system makes it challenging to…
Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens
Yosuke Kashiwagi, Hayato Futami, Emiru Tsunoo +2
In many real-world scenarios, such as meetings, multiple speakers are present with an unknown number of participants, and their utterances often overlap. We address these multi-spe…
Task Arithmetic for Language Expansion in Speech Translation
Yao-Fei Cheng, Hayato Futami, Yosuke Kashiwagi +4
Recent progress in large language models (LLMs) has gained interest in speech-text multimodal foundation models, achieving strong performance on instruction-tuned speech translatio…
Decoder-only Architecture for Streaming End-to-end Speech Recognition
Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi +2
Decoder-only language models (LMs) have been successfully adopted for speech-processing tasks including automatic speech recognition (ASR). The LMs have ample expressiveness and pe…
Rapid Language Adaptation for Multilingual E2E Speech Recognition Using Encoder Prompting
Yosuke Kashiwagi, Hayato Futami, Emiru Tsunoo +2
End-to-end multilingual speech recognition models handle multiple languages through a single model, often incorporating language identification to automatically detect the language…
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model
Hayato Futami, Siddhant Arora, Yosuke Kashiwagi +2
Recently, multi-task spoken language understanding (SLU) models have emerged, designed to address various speech processing tasks. However, these models often rely on a large numbe…
Phoneme-aware Encoding for Prefix-tree-based Contextual ASR
Hayato Futami, Emiru Tsunoo, Yosuke Kashiwagi +3
In speech recognition applications, it is important to recognize context-specific rare words, such as proper nouns. Tree-constrained Pointer Generator (TCPGen) has shown promise fo…
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions
Siddhant Arora, Hayato Futami, Jee-weon Jung +6
Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-speci…
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation
Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi +2
Collecting audio-text pairs is expensive; however, it is much easier to access text-only data. Unless using shallow fusion, end-to-end automatic speech recognition (ASR) models req…
Integration of Frame- and Label-synchronous Beam Search for Streaming Encoder-decoder Speech Recognition
Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi +2
Although frame-based models, such as CTC and transducers, have an affinity for streaming automatic speech recognition, their decoding uses no future knowledge, which could lead to…
Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding
Siddhant Arora, Hayato Futami, Yosuke Kashiwagi +3
There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework. However, prior methods often struggl…
Tensor decomposition for minimization of E2E SLU model toward on-device processing
Yosuke Kashiwagi, Siddhant Arora, Hayato Futami +6
Spoken Language Understanding (SLU) is a critical speech recognition application and is often deployed on edge devices. Consequently, on-device processing plays a significant role…
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge
Hayato Futami, Jessica Huynh, Siddhant Arora +6
This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing…
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge
Siddhant Arora, Hayato Futami, Shih-Lun Wu +6
Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken sem…
Streaming Joint Speech Recognition and Disfluency Detection
Hayato Futami, Emiru Tsunoo, Kentaro Shibata +4
Disfluency detection has mainly been solved in a pipeline approach, as post-processing of speech recognition. In this study, we propose Transformer-based encoder-decoder models tha…